Yang, C;
Xu, M;
Wang, Q;
Chen, Z;
Huang, K;
Ma, Y;
Bian, K;
... Liu, X; + view all
(2022)
FLASH: Heterogeneity-Aware Federated Learning at Scale.
IEEE Transactions on Mobile Computing
10.1109/TMC.2022.3214234.
(In press).
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Abstract
Federated learning (FL) becomes a promising machine learning paradigm. The impact of heterogeneous hardware specifications and dynamic states on the FL process has not yet been studied systematically. This paper presents the first large-scale study of this impact based on real-world data collected from 136k smartphones. We conducted extensive experiments on our proposed heterogeneity-aware FL platform namely FLASH , to systematically explore the performance of state-of-the-art FL algorithms and key FL configurations in heterogeneity-aware and -unaware settings, finding the following. (1) Heterogeneity causes accuracy to drop by up to 9.2% and convergence time to increase by 2.32×. (2) Heterogeneity negatively impacts popular aggregation algorithms, e.g., the accuracy variance reduction brought by q-FedAvg drops by 17.5%. (3) Heterogeneity does not worsen the accuracy loss caused by gradient-compression algorithms significantly, but it compromises the convergence time by up to 2.5×. (4) Heterogeneity hinders client-selection algorithms from selecting wanted clients, thus reducing effectiveness. e.g., the accuracy increase brought by the state-of-the-art client-selection algorithm drops by 73.9%. (5) Heterogeneity causes the optimal FL hyper-parameters to drift significantly. More specifically, the heterogeneity-unaware setting favors looser deadline and higher reporting fraction to achieve better training performance. (6) Heterogeneity results in non-trivial failed clients (more than 10%) and leads to participation bias (the top 30% of clients contribute 86% of computations). Our FLASH platform and data have been publicly open sourced.
| Type: | Article |
|---|---|
| Title: | FLASH: Heterogeneity-Aware Federated Learning at Scale |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TMC.2022.3214234 |
| Publisher version: | https://doi.org/10.1109/TMC.2022.3214234 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Federated learning, heterogeneity, impact analysis |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10158818 |
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