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Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies
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Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies
abstract
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. Several works have analyzed the convergence of federated learning by accounting of data heterogeneity, communication and computation limitations, and partial client participation. However, they assume unbiased client participation, where clients are selected at random or in proportion of their data sizes. In this paper, we present the first convergence analysis of federated optimization for biased client selection strategies, and quantify how the selection bias affects convergence speed. We reveal that biasing client selection towards clients with higher local loss achieves faster error convergence. Using this insight, we propose Power-of-Choice, a communication- and computation-efficient client selection framework that can flexibly span the trade-off between convergence speed and solution bias. Our experiments demonstrate that Power-of-Choice strategies converge up to 3 $\times$ faster and give $10$% higher test accuracy than the baseline random selection.
Forward citations
Cited by 7 Pith papers
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Delayed Momentum Aggregation: Communication-efficient Byzantine-robust Federated Learning with Partial Participation
D-Byz-SGDM aggregates cached momentum from non-sampled clients together with fresh momentum from sampled clients, preserving Byzantine robustness under partial participation and achieving an optimal O(cδζ²/p) stationa...
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FedSteer constructs a gradient subspace from cached client updates, projects active gradients to obtain coordinates, and reuses those coordinates on the drifted subspace to correct extreme staleness in federated learning.
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Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning
Proposes proactive client selection via differentially private mutual information and Potential Federation Loss optimized by simulated annealing to achieve faster, fairer, and more accurate federated models than unifo...
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Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning
Proactive client selection in federated learning via differentially private mutual information and simulated annealing to optimize Potential Federation Loss for utility and fairness.
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The paper derives the first convergence upper bound for split federated learning under activation upload, gradient download, and aggregation failures, then jointly optimizes client sampling and model splitting to mini...
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A carbon-aware federated learning scheduler that adds slack time, fair client selection, and fine-tuning beats a full-participation baseline on MNIST under tight carbon budgets.
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