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Federated Learning for 6G Communications:Challenges, Methods, and Future Directions

Yi Liu, Xingliang Yuan, Member, IEEE, Zehui Xiong, Jiawen Kang, Xiaofei Wang, Senior Member, IEEE, DusitNiyato, Felow, IEEE

Abstract—As the 5G communication networks are being widelydeployed worldwide, both industry and academia have started tomove beyond 5G and explore 6G communications. It is generallybelieved that 6G will be established on ubiquitous ArtificialIntelligence (AI) to achieve data-driven Machine Learning (ML)solutions in heterogeneous and massive-scale networks. However,traditional ML techniques require centralized data collection andprocessing by a central server, which is becoming a bottleneckof large-scale implementation in daily life due to significantlyincreasing privacy concerns. Federated learning, as an emergingdistributed AI approach with privacy preservation nature, isparticularly attractive for various wireless applications, especiallybeing treated as one of the vital solutions to achieve ubiquitousAI in 6G. In this article, we first introduce the integrationof 6G and federated learning and provide potential federatedlearning applications for 6G. We then describe key technicalchallenges, the corresponding federated learning methods, andopen problems for future research on federated learning in thecontext of 6G communications.

Index Terms—6G communication, federated learning, securityand privacy protection

I. INTRODUCTION

THE rapid development of wireless communication tech-niques with numerous technological innovations for

decades has greatly improved people’s lives and promotingthe development of the industry. As shown in Fig. 1, thecurrent Fourth-generation (4G) LTE network has created anera of mobile internet with Web search services, multimediaservices, and APPs as core functions [1]. The upcoming Fifth-generation (5G) system is designed to support a wider range ofservices, such as Augmented Reality/Virtual Reality (AR/VR),large-scale Internet of Things (IoT), and autonomous driv-ing [2]. Specifically, the 5G system includes three technicalcharacteristics: enhanced Mobile BroadBand (eMBB), massiveMachine-Type-Communications (mMTC), ultra-Reliable Low-Latency Communications (uRLLC). Since 5G brings unprece-dented benefits to humans and is being actively deployedaround the world, both industry and academia have begun to

Yi Liu is with School of Data Science of Technology, HeilongjiangUniversity, Harbin, China (97liuyi@ieee.org).

Xingliang Yuan is with Faculty of Information Technology, Monash Uni-versity, Clayton VIC, 3800, Australia (e-mail: xingliang.yuan@monash.edu).

Zehui Xiong and Dusit Niyato are with School of Computer Scienceand Engineering, Nanyang Technological University, Singapore. (e-mail:zxiong002@e.ntu.edu.sg, dniyato@ntu.edu.sg).

Xiaofei Wang is with the College of Intelligence and Computing, TianjinUniversity, Tianjin, China. (e-mail: xiaofeiwang@tju.edu.cn).

Jiawen Kang is with Energy Research Institute, Nanyang TechnologicalUniversity, Singapore (e-mail: kavinkang@ntu.edu.sg).

move towards the next generation of wireless technology, i.e.,Sixth-generation (6G) [1], [2].

The 5G system represents a new wireless communica-tion paradigm that adopts a service-based architecture (SBA)instead of a communication-oriented architecture (COA) toachieve “connected things”. In contrast to previous genera-tions, 6G with transformative technologies will revolutionizethe development of wireless communication from “connectedthings” to “connected intelligence” [1]. Specifically, 6G willrevolutionize technology in three areas: new media, newservices, and new infrastructures. It is expected that the 6Gsystem will adopt advanced artificial intelligence (AI) tech-nologies in these fields, and promptly and efficiently collect,transmit, and learn data anytime, anywhere to generate a largenumber of innovative applications and intelligent services [3].In particular, ubiquitous AI will empower the promising 6G,a hyper-flexible architecture that brings human-centric devel-opment concepts to all aspects of network systems, insteadof data-centric, machine-centric, and application-centric [4].Therefore, 6G communications have higher-level security andstronger privacy protection requirements.

However, traditional Machine Learning (ML) empoweredframeworks based on a central server are suffering from crit-ical security challenges, e.g., single point of failure, which isnot able to enable ubiquitous and secure AI for 6G. Moreover,due to large overhead caused by centralized data aggregationand processing, traditional centralized ML schemes might notbe suitable for ubiquitous ML [5]. Thereby, decentralized MLsolutions, in which all private data is kept in training deviceslocally, are becoming increasingly essential for 6G. Recently,Federated Learning (FL) as an emerging decentralized MLsolution has attracted particular attention from academia andindustry [2], [6]. In FL, participating devices collaborativelytrain a shared model through their local data, and thus onlyupload model updates instead of raw data to centralizedparameter servers [7].

Although FL brings high potential for AI-empowered 6Gand significantly improves privacy-sensitive applications with6G communication, FL is still in the early stages of develop-ment and is facing new challenges in 6G scenarios. In thispaper, we first introduce the core challenges of FL in 6Gcommunications including (i) large communication cost dueto multiple communication rounds for model updates and ag-gregation [8]; (ii) security problems caused by heterogeneousand diverse participating entities, e.g., poisoning attacks andbackdoor attacks [7], [9]; (iii) privacy problems resulted fromgradient leakage attacks and membership inference attacks

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Internet: Past, Present, Future

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Fig. 1. Key services and roadmap for 6G [1]–[4].

[10]; (iv) model training and inference efficiency problemsamong massive-scale 6G networks. We then propose advancedfederated learning methods to address the above challengesfrom different perspectives. Finally, we describe the openresearch topics and future directions of FL in 6G commu-nications.

II. PRELIMINARIES AND OVERVIEW

A. 6G Requirements and Use Cases

Unlike 5G communications, 6G has prominent featuresto ensure ubiquitous, seamless, intelligent, high-performanceconnectivity and networking with security and privacy pro-tection. More specifically, we will introduce the performancerequirements of 6G communications as follows.

1) High Performance Networking: It is commonly be-lieved that 6G is a complex networking system with many het-erogeneous space-air-ground-underwater communication net-works [2], [4]. The three-dimensional super-connectivity net-works provide worldwide connectivity and integrated network-ing to enable different types of network services and densecoverage through sub-networks and sub-systems, e.g., satellitecommunication networks, underwater-land communications.With the help of massive-scale heterogeneous networks, 6Gcommunications can achieve up to 1 Tbps data rate per user,ultra-low end-to-end delay, superior end-to-end reliability, andhigh energy efficiency networking [2]. Compared with 5Gcommunications, 6G communications support networking andconnecting the majority not only in dense areas but also theless dense areas, such as the underwater environment, in anefficient and low overhead manner [4]. 6G communications

employ novel communication networks to support highlydiversified data, e.g., audio, video, AR/VR data, which reachesnew communication experience with virtual networking exis-tence and involvement anywhere [2].

2) Higher Energy Efficiency: In the 6G era, there existhigher energy efficiency requirements for wireless devices withcharging constraints and battery life limitations. Therefore,long battery life and low energy consumption are two pop-ular research topics for 6G communications. To address theenergy problems of wireless devices, especially smartphones,existing studies have proposed energy harvesting technology,wireless power transfer technology, and green communicationto improve energy efficiency and extend the working time ofwireless devices [4]. Especially, the wireless devices can har-vest energy from ambient radio-frequency, solar, geothermalenergy, and wind energy by using different energy harvestingtechnologies, which can prolong the battery life. Similarly,the wireless devices with wireless charging equipment canobtain energy supplement from dense network infrastructuresor mobile charging stations, e.g., Unmanned Aerial Vehicle(UAV), Electric Vehicles (EVs), through wireless power trans-fer technology.

For green communication techniques, AI-based solutionsare quite important to optimize energy usage and energyscheduling for wireless devices in a dynamic environment andcomplex optimization goals. Advanced machine learning tech-niques, such as deep reinforcement learning, can be utilized tooptimize the computation task offloading decision of a wirelessdevice, and also make the best scheduling solution of workingand sleeping time, which can lower energy consumption and

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enhance energy efficiency. The AI-based solutions can be alsoapplied in multi-hop information routing in cooperative relaycommunication and communication infrastructure deploymentin network-densification 6G scenarios, which significantlyreduces the transmit power of the wireless devices withoutlong propagation distance thus enabling high-efficiency com-munication [2], [4].

3) High Security and Privacy: Existing research mainlyfocuses on network throughput, reliability, and delay in 4Gand 5G communications [4]. However, in the past few decades,wireless communication security and privacy issues have beenignored to some extent. Since data security and privacy issuesare closely related to users’ lives, protecting data security andprivacy has become a very important part of human-centric6G communications. Meanwhile, communication/data serviceproviders legally collect a large amount of user information,which leads to frequent leakage of privacy data. In order tosolve this problem, it is envisaged that FL techniuqes canbe used to achieve privacy-enhanced deep learning in 6Gnetworks.

4) High Intelligence: The high intelligence of 6G will bebeneficial to provide users with high-quality, personalized, andintelligent services. High-intelligent 6G includes operationalintelligence, application intelligence, and service intelligenceas follows.• Operational Intelligence. Traditional network operations

involve a series of resource optimization and multi-objective performance optimization problems [1]. In orderto achieve a satisfactory level of network operation, opti-mization methods based on game theory, contract theory,etc. are widely used. However, these optimization theoriesmay not obtain the optimal solution in large-scale time-varying variables and multi-objective scenarios. With thedevelopment of deep learning technologies, the abovecan be solved by using advanced machine learning tech-nologies. On the other hand, the emergence of federatedlearning has transformed the multi-objective linear opti-mization problems into a nonlinear optimization problem,thus finding out the best solution for complex and time-varying decisions in operational intelligence [2].

• Application Intelligence. At present, applications relatedto 5G networks are gradually becoming intelligent. For6G networks, intelligent applications are one baselineof application requirements [9], [11]. FL empoweredwireless communication technologies to enable devices toconnect with 6G networks to run a variety of intelligentapplications. For example, in the future, users may needintelligent voice assistants to complete their daily sched-ules [12]. The 6G network ubiquitous AI will provideusers with highly intelligent applications.

• Service Intelligence Furthermore, as a human-centricnetwork, the high intelligence of the 6G network willprovide intelligent services in a satisfactory and person-alized manner [1], [2], [4]. For example, FL providesusers with personalized healthcare services, personalizedrecommendation services, and personalized intelligentvoice services in a distributed learning manner. In thefuture, intelligent services will be tightly integrated with

the 6G networks [4].

B. Typical Use Scenarios

Compared with previous generations, the 5G service modelhas been transformed into a service-based architecture, andits user cases include: enhanced Mobile Broad Band (eMBB),massive Machine-Type-Communications (mMTC), and ultra-Reliable Low-Latency Communications (uRLLC). As shownin Fig. 2, driven by Industry 5.0 and deep learning technolo-gies, 6G will provide the following new service types:• New Media. With the rapid development of wireless

network communication technologies, it can be expectedthat the form of information interaction will graduallyevolve from AR/VR to high fidelity extended reality (XR)interaction after 10 years, and even realizing wirelessholographic communication [1]–[4]. Users can enjoy thenew services brought by holographic communicationand holographic display anytime and anywhere, such asvirtual education, virtual tourism, virtual sports, virtualpainting, virtual concerts, and other fully immersive holo-graphic experiences.

• New Services. According to ITU-T’s 6G communica-tion technology white paper, beyond and high-precisionteleport technology will provide users with a variety ofnew services [1]–[4], [13]. Holographic teleport, quantumcommunication, visible light communication (VLC), andother communication technologies have subverted thetraditional service model. For example, industries suchas remote surgery [14], cloud PLC [15], and intelligenttransportation systems [16] will be empowered by thenew service model to provide users with better ser-vices. The goal of these new technologies is to providehigh-precision services, deterministic service, and best-guaranteed services.

• New Infrastructure. With the development of deeplearning technologies, the 6G communication system hasspawned many emerging infrastructures such as Inte-grated Terrestrial and Space [15], federated learning net-works [11], decentralized infrastructures [1], and trustableinfrastructure [4]. In particular, the FL network benefitsfrom the high bandwidth and low latency of the 6Gnetwork, which has brought many emerging intelligentapplications to cities, factories, and people.

C. Federated Learning

In this subsection, we introduce an FL-based distributedlearning architecture in 6G. In this architecture, a large numberof decentralized devices associated with different services cancollaboratively train a shared global model (e.g., anomalydetection, recommendation system, next-word prediction, etc.)by using locally collected datasets.

As shown in Fig. 3, the procedure of FL-based architectureis divided into three phases: the initialization, the training, andthe aggregation phase. In the initialization phase, a device willevaluate its service requests, needs, and connection conditions,and decides whether to register with the nearest cloud to

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New Services

New Media

Remote Surgery

Cloud PLC

Intelligent Transportation System

Virtual Education

Virtual Painting

Virtual Tourism

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Human-centric 6G Communications

Fig. 2. Key services and roadmap for 6G [17]–[19].

join the training of the shared global model via a wiredor wireless connection (e.g., 6G). Then, the cloud actingas task publisher will randomly select a subset of devicesfrom the registered devices to participate in this round oftraining, and reject the remaining registered devices. Thecloud will also send initialized or pre-trained global modelωt to each selected device (steps 1©, 2©). In the trainingphase, each selected device trains global model ωk

t ← ωt

by using local dataset to obtain the updated global modelωk+1t in each iteration. In particular, for the k-th device

(k ∈ {1, 2, . . . ,K}), the loss function needs to be optimizedas follows: argmin

ω∈RFk(ω), Fk(ω) =

1Dk

∑i∈Dk

fi(ω), whereDk denotes the size of local dataset that contains input-outputvector pairs (xi, yi), xi, yi ∈ R, ω is local model parameter,and fi(ω) is a local loss function (e.g., fi(ω) = 1

2 (xiTω−yi)).

Each selected device uploads the model updates to the cloud(steps 3©, 4©, 5©). In the aggregation phase, the cloud receivesmodel updates of all selected devices for aggregation toobtain a new global model ωt+1 for the next iteration, i.e.,

ωt+1 ← ωt − 1K

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Fk(ω), where K denotes the number

of edge nodes. In the next round, the device selected by thecloud downloads the current latest global model ωt+1 from thecloud. The device will use the received new global model toupdate its respective model. In the next round of training, thecloud will randomly select a new device subset and repeat theabove process until the trained model converges or meets thestopping criteria (step 6©). FL empowers the ubiquitous 6G,which allows the learned model to serve a wider geographicarea.

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Fig. 3. An overview of federated learning process in 6G [20].

III. CORE CHALLENGES FOR FEDERATED LEARNING IN6G

In this section, we introduce the core challenges of FL,which are the main bottleneck problem before large-scaledeployment of FL in 6G applications.

A. Challenge 1: Expensive Communication

Since the FL involves thousands of devices participatingduring model training, communication is a critical bottleneckfor FL being widely used in 6G [11]. Previous studies [6],[8], [10], [20]–[24] has made many efforts to improve thecommunication efficiency of FL system. Furthermore, it ischallenging for FL networks to achieve communication in theFL networks is synchronized with the local calculation of thedevice [8], [10], [25]. To make the FL model suitable for 6G

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networks with massive, heterogeneous devices and networks,it is necessary to develop a communication-efficient method,which can greatly reduce the number of gradients exchangedbetween the devices and the cloud instead of all gradientsinformation. In order to further reduce communication over-head in this setting, two key aspects need to be considered:(i) reducing the total number of communication rounds, or(ii) reducing the number of gradients in each communicationround.

B. Challenge 2: Security Problems

Since 6G networks can provide ubiquitous services acrossa wider geographic area, the computing and communicationcapabilities of each device in the network may vary due tochanges in hardware (CPU, GPU), network connectivity (4G,5G, 6G, WiFi), and energy (battery level). Obviously, thesystem heterogeneity between the devices will bring someconfusion and faults to the FL model and 6G network [9], [11].Additionally, there may be unreliable devices in the FL, whichmay cause the Byzantine failure of the system. Similarly,adversaries may launch active learning-based attacks (like poi-soning attacks and backdoor attacks) on heterogeneous devicesand cause errors in the FL system. The security vulnerabilitiesof these FL systems greatly exacerbate challenges such asmitigating attacks, tolerance, and faults. Therefore, developinga secure and robust FL must: (i) defend against maliciousattacks, (ii) tolerate heterogeneous hardware, and (iii) achieverobust aggregation algorithms.

C. Challenge 3: Privacy Concerns

Although FL protects the privacy of each device by shar-ing model updates (e.g., gradients information) instead ofthe raw data, the private data will still be disclosed duringthe interaction between the device and the cloud [26]. Forexample, adversaries will launch membership inference [26]or gradient leakage attacks [27] to steal local training datafrom the devices. Previous work has focused on using toolssuch as secure multi-party (SMC) computing or homomorphicencryption (HE) to enhance the privacy of FL, but thesemethods cannot address the above malicious attacks [11]. SMCand HE can only prevent data breach problems and cannotresist member inference attacks and gradient leak attacks.Therefore, it is very urgent for the FL system to developnew privacy-enhancing techniques to resist or mitigate theaforementioned malicious attacks.

D. Challenge 4: Effective Issues

Deploying FL models to devices generally involves modeltraining and inference [11]. If the speed of model trainingand reasoning is relatively slow, users will not be able toexperience real-time intelligent services [28]. Therefore, whenFL systems are widely deployed in 6G networks, they willencounter the following challenges: (i) the size of the FLmodel is too large to adapt to a single device; (ii) the FLmodel training is too slow to meet the delay requirementsof the 6G network; (iii) the FL model inference is too slow

to satisfy the user’ real-time demand. Efficient training andinference are necessary for the perfect integration of FL and6G networks. However, it is challenging for FL systems toachieve efficient model training and inference in a massive,heterogeneous network.

IV. ADVANCED FEDERATED LEARNING METHODS FOR 6G

To address the aforementioned challenges, we propose ad-vanced federated learning systems through different emergingtechnologies or methods to enable communication-efficient,secure, and privacy-enhanced federated learning, respectively.

A. Communication-efficient Federated Learning For 6G

In 6G, it is challenging for devices that span a largergeographic area to obtain a better global model but withhuge communication overhead. The communication overheadwill affect the gradient exchange between the devices andthe cloud, thus affecting the model aggregation at the cloud.Therefore, we need to find a more efficient way to achieve FLtraining. In this subsection, we will explain communication-efficient FL from the perspectives of system-level and algo-rithm level, which promotes a wider-range FL deployment andusage for 6G communications.

1) Communication-efficient FL: System Level: From asystem perspective, data distribution (e.g., non-independentand identical distribution), device distribution (e.g., hetero-geneous devices across regions and networks), computationmethods (e.g., decentralized and centralized), and commu-nication mechanisms (e.g., synchronous and asynchronousscheme) have different impacts on communication efficiencyin different application scenarios [8].• Asynchronous FL System: As shown in Fig. 4, AFLS

can reduce the computation time of the devices byasynchronously aggregating the model updates, therebyimproving the communication efficiency of FL. Let κ =

CommComp+Comm , where κ represents communication effi-ciency, Comp is the communication time, and Comp isthe computation time. It can be seen from Fig. 4 that theComp of asynchronous model update scheme is shorterthan that of synchronous one, so the communicationefficiency κ of the asynchronous model update schemeis higher than that of the synchronous one.

2) Communication-efficient FL: Algorithm Level: At thealgorithm level, achieving communication-efficient FL canreduce the communication rounds of training a model byaccelerating convergence [25] and reduce the communicationcost of each round by using gradient compression techniques[10] (e.g., sparsification, quantization, etc.). More details aredescribed below.• Accelerating Model Convergence: Stochastic gradient

descent (SGD) algorithms based on zero-order, first-order,second-order, and federated optimization are used toreduce the number of rounds of model training [8]. Sincethe federated optimization method can protect the privatedata on each device, it is very popular with this uniquemotivation in accelerating training model convergence.

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Device 1

Device 2

Device 3

Device 4

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Synchronous Federated Learning Asynchronous Federated Learning

: Computation : Communication

Fig. 4. The overview of the synchronous and asynchronous FL.

• Reducing Communication Overhead: Gradient spar-sification and gradient quantization can greatly reducethe large number of gradients exchanged between thedevices and the cloud to achieve communication-efficientFL. Lin et al. in [10] proposed a Top-k selection-basedgradient compression scheme to improve communicationefficiency. In this scheme, the authors can compress thegradient 300 times to reduce the number of gradientswithout compromising accuracy.

B. Secure Federated Learning For 6G

Due to the wide range of 6G network connections, FLwill suffer malicious attacks from heterogeneous networks,heterogeneous devices, and malicious participants during thetraining process [9]. To alleviate this problem, researchershave proposed many different defense solutions from threeperspectives: aggregation algorithm, detection mechanism, andreputation management.

1) Robust Aggregation Algorithm: Aggregation is a veryimportant operation in the FL training process that directlyaffects the results of model convergence. The motivation of therobust aggregation algorithm is to greatly reduce the impactof low-quality model updates generated by malicious devices(i.e., poisoning attacks) on global model training. Furthermore,this method can make the cloud tolerate Byzantine failuresof some devices [29], [30]. For example, Ang et al. in [31]proposed the regularizer approximation method to reduce thenoise interference of heterogeneous devices and heterogeneousnetworks.

2) Robust Detection Mechanism: Another intuitive idea isto detect malicious devices to prevent them from participatingin FL training. Such a mechanism has generally utilized theaccuracy of the sub-model generated by the device as anevaluation metric to detect malicious devices. Liu et al. in[9] utilized the smart contract techniques in the blockchain todesign a malicious device detection mechanism to alleviate themalicious attack problems.

3) Reliable Reputation Management: The historical be-haviors of the devices can be used as a key indicator toevaluate its reliability and trustworthiness by a metric namedreputation. The high reputation value indicates more reliabledevices. Inspired by this, establishing a reputation managementscheme for device historical behaviors in FL can also preventmalicious devices from damaging the global model. Kanget al. in [12] proposed a reputation management scheme to

calculate the historical reputation of the devices to achieve arobust FL with high-reputation devices.

C. Privacy-preserving Federated Learning For 6G

1) Differentially Privacy: Differential privacy (DP) [32]techniques are proposed to protect the privacy of gradientinformation, thereby achieving cloud-level privacy protection.Geyer et al. in [33] applied the DP technique in FL system thatprotects cloud-level privacy. Similarly, in order to protect user-level privacy, the local differential privacy (LDP) techniquesachieve this goal by disturbing the gradients uploaded by thedevices [26]. However, DP and LDP technologies enhanceFL privacy at the expense of model performance. Therefore,there are currently advanced methods that balance privacy andperformance as described below.

2) Deep Net Pruning: Neural network pruning is a tech-nique of deep learning whose goal is to develop a smallerand more efficient neural network. Recently, Huang et al.[34] utilized pruning as an equivalent technique of DP toprotect the privacy of the FL system while ensuring the modelperformance. Such a method creates a new idea of using modelpruning to be equivalent to DP techniques, which provides newopportunities for balancing utility and privacy.

3) Gradient Compression: The reason why adversariescan infer the local data of the devices is that the gradientinformation contains rich semantic information [27]. Inspiredby the above, an intuitive idea is that the methods that disruptthe distribution of gradient information thus protecting thegradient privacy. Zhu et al. in [27] proved that gradient com-pression can defend against gradient leakage attacks withoutcompromising accuracy and the defense effect is better thanthat of DP.

D. Effective Federated Learning For 6G

The long-term goal of human-centric communication ser-vices in 6G networks is to handle user needs in real time.Therefore, it is necessary to achieve efficient FL from trainingand inference.

1) Efficient Training: Efficient training can greatly reducethe training time of mobile devices to achieve efficient FL.The advanced training methods are summarized as follows.• Federated Parallelization: Data and model paralleliza-

tion are generally used to accelerate model training.Data parallization achieves efficient training by runningmultiple training samples in parallel [35], [36]. Modelparallelization accelerates model training by splitting themodel over multiple processors [28].

• Federated Distillation: Model distillation adopts transferlearning to utilize the output of a pre-trained complexmodel (i.e., Teacher model) as a supervised signal to trainanother simple network, i.e., Student model. Such a waycan train student models to improve the efficiency ofmodel training. Jeong et al. in [22] proposed federateddistillation (FD), an efficient distributed model trainingalgorithm, whose training efficiency is much smaller thanthe FL benchmark scheme, especially when the modelsize is large.

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2) Efficient Inference:: The size of the existing FL modelis too large to realize real-time inference on the devices.Efficient inference can be achieved in the following ways.• Pruning: The pruning technique is a model optimization

technique that includes removing excess weights in theweight tensor. The compressed neural network not onlyruns faster but also reduces the computational cost of thetraining network, which is a critical step in deploying themodel to mobile phones or other edge devices.

• Weight Sharing. Weight sharing reduces the number ofmodel parameters by sharing weights, thereby achievingefficient model inference. The reason is that the fewerthe parameters of the model, the smaller the model size.Tran et al. in [23] utilized weight sharing approach forwireless networks to improve model inference efficiency.

V. OPEN RESEARCH TOPICS AND FUTURE DIRECTIONS

A. Trustworthy Federated Learning

1) Privacy-enhanced Federated Learning: Previous workabout FL has covered user or cloud-level privacy for alldevices in the 6G networks. However, in practice, the previousschemes provide strict privacy restrictions at the expenseof accuracy [11]. It is essential for FL to develop privacy-enhanced techniques that do not compromise accuracy toprovide strict privacy guarantees because the industry is veryconcerned about the accuracy of the FL model. To this end,few studies are exploring potential solutions. For example,Huang et al. [34] recently proposed a net pruning technique toprovide strict privacy guarantees by replacing pruning with DPtechnique, and also to improve the training efficiency of themodel. It is an interesting and ongoing direction to developingmethods that can balance efficiency and privacy restrictions infuture work.

2) Security-enhanced Federated Learning: Since the FLsystems normally involve multiple entities of devices, cloud,and machine learning model providers, it is vulnerable tomalicious attacks from adversaries against different entities.Although existing work has made a lot of efforts to providestrong security protection for the FL systems, there is littlework to defend or mitigate these malicious attacks from thesystem perspective. Bonawitz et al. in [24] explored severalmore secure and robust aggregation algorithms and faulttolerance mechanisms from the perspective of system design.The serurity-enhanced techniques are designed from a systemperspective so that FL can develop more practical industrialapplications with the help of 6G networks.

3) Fair Federated Learning: FL involves thousands ofdevices training a shared global model in massive, heteroge-neous networks [37]. Naive optimizing the global model insuch a network may be unfair to some devices by causingdisproportionate advantages or disadvantages. Obviously, FLtowards fairness is an indispensable requirement for human-centric 6G communication services. Specifically, a fair FLin a wireless network involves fair resource allocation and areasonable incentive mechanism. How to allocate computingand communication resources accurately and fairly in massive,heterogeneous networks has become a critical challenge that

needs to be solved urgently. Some pioneering work, Li etal. in [37] proposed q-Fair FL (q-FFL), which is a newaggregation algorithm to achieve a fair allocation of resourcesand accuracy.

4) Explainable Federated Learning: The vast majority ofFL models are black-box models (i.e., without interpretability),which makes users unable to understand what kind of servicesthe model provides for themselves. In a complex 6G networksystem, the unexplainable predictions or decisions output bythe black-box model may cause huge losses to users. Forexample, 6G-supported self-driving relies on an on-vehiclevisual recognition model to determine whether the vehicleis running or stopped. Since the on-vehicle model has nointerpretability, the driver cannot understand the decision ofthe vehicle model output. In 2018, the self-driving vehicledeveloped by Uber caused a car accident due to the wrongoutput of the on-vehicle black-box model 1. Therefore, inthe context of a complex network system, such as 6G, thedevelopment of an interpretable FL model is the necessaryway to human-centric communication services.

B. Efficient and Effective Federated Learning1) Novel Asynchronous System: Even though the 6G

network can bring the advantage of extremely low latencyto the FL systems, the communication overhead is still thebottleneck of the FL systems being widely used [11]. Asdescribed in Section IV-A1, the two most commonly studiedcommunication optimization schemes in distributed machinelearning systems are the batch synchronous method and theasynchronous method (where the delay of the model update isassumed to be bounded) [24]. Indeed, asynchronous commu-nication schemes involve scheduler, coordinator, worker, andupdater, so there are several optimization problems for theseroles that can be considered in the future: i) how the sched-uler reasonably schedule the communication and computingresources in the systems; ii) how the coordinator efficientlycontrol the working state and idle state of the devices; iii)how workers and updaters optimize hyperparameters for modelupdates. These optimization problems are worth studying infuture work in order to develop novel asynchronous systems.

2) Neural Architecture Search: The structure of the cur-rent FL models is generally predefined, but this predefinedarchitecture may not be the best choice because it may not besuitable for non-independent and identical distribution (non-IID) data. Therefore, the Neural Architecture Search (NAS)-based Automating FL (AutoFL) schemes may be a promisingsolution to this problem. For example, a study in [38] proposeda federated NAS (FedNAS) algorithm to help distributeddevices collaborate to find a better architecture with higheraccuracy. NAS provides opportunities for seeking a better FLmodel architecture in the future.

C. Towards Incentive Federated LearningExisting studies mainly focus on enhancing the performance

of FL algorithms, e.g., accuracy and training time. Neverthe-less, an optimistic assumption, that all the data owners are

1http://tech.sina.com.cn/zt˙d/uberincident/

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willing to join the FL anytime and anywhere, is not practicalin 6G scenarios with massive self-interest devices. As a result,incentive mechanisms for honest and active participation area core and urgent research topic [7], [21], [39]–[42]. Someinteresting topics include: i) Due to information asymmet-ric between task publishers and participating devices, e.g.,information about time-varying available resources, unfixedworking periods and changeable participation willingness, itis still an open issue to design effective online-learning basedincentive mechanisms to remove the impacts of both infor-mation asymmetric and time-varying factors, and also ensureefficient federated learning in 6G scenarios; ii) Consideringheterogeneous and massive devices with diverse hardwareequipment in 6G scenarios, the data quality of the devices isdiverse. But the data quality plays an important role in learningperformance. It is a challenging problem how to design dataquality-based incentive mechanisms to motivate more deviceswith high-quality data to participate in federated learning andobtain higher rewards for their high-quality data contributions,thus improving both the system reliability and the learningperformance [7], [39], [41].

D. Towards Personalized Federated Learning

It is challenging for the FL system in the 6G networkto provide users with personalized services. Prior studies[43]–[46] adopt different personalized techniques to provideusers with real-time personalized services, which is a solidstep towards personalized FL. However, personalized FL stillfaces challenges from non-IID data, system heterogeneity,and network heterogeneity. Personalized service is a veryimportant part of the human-centric 6G services. Therefore,it is an interesting and meaningful topic for FL to seek novelways to address the above challenges.

VI. CONCLUSION

In this article, we provided an overview of integratingfederated learning into 6G communications. We discussed therequirements of 6G communication and core challenges of fed-erated learning for 6G applications. For the above challenges,we provided a comprehensive introduction of the emergingadvanced federated learning methods for 6G communications,which including communication-efficient federated learning,secure federated learning, and effective federated learning.Finally, we outlined out a handful of open problems anddirections worth future research efforts.

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