Time-triggered Federated Learning over Wireless Networks

TY - JOUR

T1 - Time-triggered Federated Learning over Wireless Networks

AU - Zhou, Xiaokang

AU - Deng, Yansha

AU - Xia, Huiyun

AU - Wu, Shaochuan

AU - Bennis, Mehdi

N1 - “© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”

PY - 2022/7/15

Y1 - 2022/7/15

N2 - The newly emerging federated learning (FL) framework offers a new way to train machine learning models in a privacy-preserving manner. However, traditional FL algorithms are based on an event-triggered aggregation, which suffers from stragglers and communication overhead issues. To address these issues, in this paper, we present a time-triggered FL algorithm (TT-Fed) over wireless networks, which is a generalized form of classic synchronous and asynchronous FL. Taking the constrained resource and unreliable nature of wireless communication into account, we jointly study the user selection and bandwidth optimization problem to minimize the FL training loss. To solve this joint optimization problem, we provide a thorough convergence analysis for TT-Fed. Based on the obtained analytical convergence upper bound, the optimization problem is decomposed into tractable sub-problems with respect to each global aggregation round, and finally solved by our proposed online search algorithm. Simulation results show that compared to asynchronous FL (FedAsync) and FL with asynchronous user tiers (FedAT) benchmarks, our proposed TT-Fed algorithm improves the converged test accuracy by up to 12.5% and 5%, respectively, under highly imbalanced and non-IID data, while substantially reducing the communication overhead.

AB - The newly emerging federated learning (FL) framework offers a new way to train machine learning models in a privacy-preserving manner. However, traditional FL algorithms are based on an event-triggered aggregation, which suffers from stragglers and communication overhead issues. To address these issues, in this paper, we present a time-triggered FL algorithm (TT-Fed) over wireless networks, which is a generalized form of classic synchronous and asynchronous FL. Taking the constrained resource and unreliable nature of wireless communication into account, we jointly study the user selection and bandwidth optimization problem to minimize the FL training loss. To solve this joint optimization problem, we provide a thorough convergence analysis for TT-Fed. Based on the obtained analytical convergence upper bound, the optimization problem is decomposed into tractable sub-problems with respect to each global aggregation round, and finally solved by our proposed online search algorithm. Simulation results show that compared to asynchronous FL (FedAsync) and FL with asynchronous user tiers (FedAT) benchmarks, our proposed TT-Fed algorithm improves the converged test accuracy by up to 12.5% and 5%, respectively, under highly imbalanced and non-IID data, while substantially reducing the communication overhead.

KW - Computational modeling

KW - Convergence

KW - convergence analysis

KW - Federated learning

KW - resource allocation

KW - Servers

KW - Synchronization

KW - Training

KW - Wireless communication

KW - Wireless networks

UR - http://www.scopus.com/inward/record.url?scp=85135204752&partnerID=8YFLogxK

U2 - 10.1109/TWC.2022.3189601

DO - 10.1109/TWC.2022.3189601

M3 - Article

AN - SCOPUS:85135204752

SN - 1536-1276

SP - 1

JO - IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS

JF - IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS

ER -