TY - CHAP
T1 - Resource Allocation for Time-triggered Federated Learning over Wireless Networks
AU - Zhou, Xiaokang
AU - Deng, Yansha
AU - Xia, Huiyun
AU - Wu, Shaochuan
AU - Bennis, Mehdi
N1 - Funding Information:
This work was supported by the Natural Science Foundation of China under Grants 61671173, 6217011221, and 61831002. The work of Xiaokang Zhou was supported by the China Scholarship Council.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
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 generalization of classic synchronous and asynchronous FL. Taking the resource-constrained and unreliable nature of wireless networks into account, we jointly consider the user selection and bandwidth optimization problem to minimize the FL training loss. The optimization problem is decomposed into tractable sub-problems with respect to each global aggregation round, and finally solved by our proposed greedy search algorithm. Simulation results show that compared to asynchronous FL (FedAsync) and FL with asynchronous 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 generalization of classic synchronous and asynchronous FL. Taking the resource-constrained and unreliable nature of wireless networks into account, we jointly consider the user selection and bandwidth optimization problem to minimize the FL training loss. The optimization problem is decomposed into tractable sub-problems with respect to each global aggregation round, and finally solved by our proposed greedy search algorithm. Simulation results show that compared to asynchronous FL (FedAsync) and FL with asynchronous 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.
UR - http://www.scopus.com/inward/record.url?scp=85130493462&partnerID=8YFLogxK
U2 - 10.1109/ICC45855.2022.9838329
DO - 10.1109/ICC45855.2022.9838329
M3 - Conference paper
AN - SCOPUS:85130493462
T3 - IEEE International Conference on Communications
SP - 2810
EP - 2815
BT - ICC 2022 - IEEE International Conference on Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Communications, ICC 2022
Y2 - 16 May 2022 through 20 May 2022
ER -