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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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FedADMM: A federated primal-dual algorithm allowing partial participation
Federated learning is a framework for distributed optimization that places emphasis on communication efficiency. In particular, it follows a client-server broadcast model and is appealing because of its ability to accommodate heterogene- ity in client compute and storage resources, non-i.i.d. data assumptions, and data privacy. Our contribution is to offer a new federated learning algorithm, FedADMM, for solving non-convex composite optimization problems with non-smooth regularizers. We prove the convergence of FedADMM for the case when not all clients are able to participate in a given communication round under a very general sampling model.
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- Award ID(s):
- 2144634
- PAR ID:
- 10390334
- Date Published:
- Journal Name:
- Proceedings of the IEEE 61st Conference on Decision and Control (CDC)
- Page Range / eLocation ID:
- 287-294
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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