TY - CHAP
T1 - Asynchronous Federated Learning via Over-the-Air Computation
AU - Zheng, Zijian
AU - Deng, Yansha
AU - Liu, Xiaonan
AU - Nallanathan, Arumugam
N1 - Funding Information:
This work was supported in part by UKRI under the UK government’s Horizon Europe funding guarantee (grant number 10061781), as part of the European Commission-funded collaborative project VERGE, under SNS JU program (grant number 101096034). This work is also a contribution by Project REASON, a UK Government funded project under the FONRC sponsored by the DSIT.
Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The emerging field of federated learning (FL) provides great potential for edge intelligence while protecting data privacy. However, as the system grows in scale or becomes more heterogeneous, new challenges, such as the spectrum shortage and stragglers issues, arise. These issues can potentially be addressed by over-the-air computation (AirComp) and asynchronous FL, respectively, however, their combination is difficult due to their conflicting requirements. In this paper, we propose a novel asynchronous FL with AirComp in a time-triggered manner (async-AirFed). The conventional async aggregation requests the historical data to be used for model updates, which can cause the accumulation of channel noise and interference when AirComp is applied. To address this issue, we propose a simple but effective truncation method which retains a limited length of historical data. Convergence analysis presents that our proposed async-AirFed converges on non-convex optimality function with sub-linear rate. Simulation results show that our proposed scheme achieves more than 34% faster convergence than the benchmarks, by achieving an accuracy of 85%, which also improves the time utilization efficiency and reduces the impact of staleness and the channel.
AB - The emerging field of federated learning (FL) provides great potential for edge intelligence while protecting data privacy. However, as the system grows in scale or becomes more heterogeneous, new challenges, such as the spectrum shortage and stragglers issues, arise. These issues can potentially be addressed by over-the-air computation (AirComp) and asynchronous FL, respectively, however, their combination is difficult due to their conflicting requirements. In this paper, we propose a novel asynchronous FL with AirComp in a time-triggered manner (async-AirFed). The conventional async aggregation requests the historical data to be used for model updates, which can cause the accumulation of channel noise and interference when AirComp is applied. To address this issue, we propose a simple but effective truncation method which retains a limited length of historical data. Convergence analysis presents that our proposed async-AirFed converges on non-convex optimality function with sub-linear rate. Simulation results show that our proposed scheme achieves more than 34% faster convergence than the benchmarks, by achieving an accuracy of 85%, which also improves the time utilization efficiency and reduces the impact of staleness and the channel.
KW - Asynchronous federated learning
KW - error accumulation
KW - over-the-air computation
UR - http://www.scopus.com/inward/record.url?scp=85187387245&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM54140.2023.10437951
DO - 10.1109/GLOBECOM54140.2023.10437951
M3 - Conference paper
AN - SCOPUS:85187387245
T3 - Proceedings - IEEE Global Communications Conference, GLOBECOM
SP - 1345
EP - 1350
BT - GLOBECOM 2023 - 2023 IEEE Global Communications Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Global Communications Conference, GLOBECOM 2023
Y2 - 4 December 2023 through 8 December 2023
ER -