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With a Little Help from My Friend: Server-Aided Federated Learning with Partial Client Participation
Haibo Yang · Peiwen Qiu · Prashant Khanduri · Jia Liu
Event URL: https://openreview.net/forum?id=xT6d2Ghtkv »

Although federated learning (FL) has been a prevailing distributed learning framework in recent years due to its benefits in scalability/privacy and rich applications in practice, there remain many challenges in FL system design, such as data and system heterogeneity. Notably, most existing works in the current literature only focus on addressing data heterogeneity issues (e.g., non-i.i.d. datasets across clients), while often assuming either full client or uniformly distributed client participation. However, such idealistic assumptions on client participation rarely hold in practical FL systems. It has been frequently found in FL systems that some clients may never participate in the training (aka partial/incomplete participation) due to various reasons. This motivates us to fully investigate the impacts of incomplete FL participation and develop effective mechanisms to mitigate such impacts. Toward this end, by establishing a fundamental generalization error lower bound, we first show that conventional FL is {\em not} PAC-learnable under incomplete participation. To overcome this challenge, we propose a new server-aided federated learning (SA-FL) framework with an auxiliary dataset deployed at the server, which is able to revive the PAC-learnability of FL under incomplete client participation. Upon resolving the PAC-learnability challenge, we further propose the SAFARI (server-aided federated averaging) algorithm that enjoys convergence guarantee and the same level of communication efficiency and privacy as state-of-the-art FL.

Author Information

Haibo Yang (Ohio State University)
Peiwen Qiu (The Ohio State University, Columbus)
Prashant Khanduri (University of Minnesota)
Jia Liu (The Ohio State University)
Jia Liu

Jia (Kevin) Liu is an Assistant Professor in the Dept. of Electrical and Computer Engineering at The Ohio State University and an Amazon Visiting Academics (AVA). He received his Ph.D. degree from the Dept. of Electrical and Computer Engineering at Virginia Tech in 2010. From Aug. 2017 to Aug. 2020, he was an Assistant Professor in the Dept. of Computer Science at Iowa State University. His research areas include theoretical machine learning, stochastic network optimization and control, and performance analysis for data analytics infrastructure and cyber-physical systems. Dr. Liu is a senior member of IEEE and a member of ACM. He has received numerous awards at top venues, including IEEE INFOCOM'19 Best Paper Award, IEEE INFOCOM'16 Best Paper Award, IEEE INFOCOM'13 Best Paper Runner-up Award, IEEE INFOCOM'11 Best Paper Runner-up Award, IEEE ICC'08 Best Paper Award, and honors of long/spotlight presentations at ICML, NeurIPS, and ICLR. He is an NSF CAREER Award recipient in 2020 and a winner of the Google Faculty Research Award in 2020. He received the LAS Award for Early Achievement in Research at Iowa State University in 2020, and the Bell Labs President Gold Award. His research is supported by NSF, AFOSR, AFRL, and ONR.

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