Federated learning is a distributed optimization paradigm that enables a large number of resource-limited client nodes to cooperatively train a model without data sharing. Previous works analyzed the convergence of federated learning by accounting for data heterogeneity, communication/computation limitations, and partial client participation. However, most assume unbiased client participation, where clients are selected such that the aggregated model update is unbiased. In our work, we present the convergence analysis of federated learning with biased client selection and quantify how the bias affects convergence speed. We show that biasing client selection towards clients with higher local loss yields faster error convergence. From this insight, we propose Power-of-Choice, a communication- and computation-efficient client selection framework that flexibly spans the trade-off between convergence speed and solution bias. Extensive experiments demonstrate that Power-of-Choice can converge up to 3 times faster and give 10% higher test accuracy than the baseline random selection.
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This content will become publicly available on June 1, 2024
FedSysID: A Federated Approach to Sample-Efficient System Identification
We study the problem of learning a linear system model from the observations of M clients. The catch: Each client is observing data from a different dynamical system. This work addresses the question of how multiple clients collaboratively learn dynamical models in the presence of heterogeneity. We pose this problem as a federated learning problem and characterize the tension between achievable performance and system heterogeneity. Furthermore, our federated sample complexity result provides a constant factor improvement over the single agent setting. Finally, we describe a meta federated learning algorithm, FedSysID, that leverages existing federated algorithms at the client level.
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- Award ID(s):
- 2231350
- NSF-PAR ID:
- 10463222
- Editor(s):
- Matni, N.; Morari, M; Pappas, G.
- Date Published:
- Journal Name:
- Proceedings of Machine Learning Research
- Volume:
- 211
- ISSN:
- 2640-3498
- Page Range / eLocation ID:
- 1308--1320
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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