The paper designs a federated learning incentive scheme that pays agents based on the total variation distance of their label distribution from an IID reference, and claims this induces high-quality participation.
On the Convergence of Federated Averaging with Cyclic Client Participation
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abstract
Federated Averaging (FedAvg) and its variants are the most popular optimization algorithms in federated learning (FL). Previous convergence analyses of FedAvg either assume full client participation or partial client participation where the clients can be uniformly sampled. However, in practical cross-device FL systems, only a subset of clients that satisfy local criteria such as battery status, network connectivity, and maximum participation frequency requirements (to ensure privacy) are available for training at a given time. As a result, client availability follows a natural cyclic pattern. We provide (to our knowledge) the first theoretical framework to analyze the convergence of FedAvg with cyclic client participation with several different client optimizers such as GD, SGD, and shuffled SGD. Our analysis discovers that cyclic client participation can achieve a faster asymptotic convergence rate than vanilla FedAvg with uniform client participation under suitable conditions, providing valuable insights into the design of client sampling protocols.
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cs.AI 1years
2025 1verdicts
REJECT 1representative citing papers
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Incentivizing High-quality Participation From Federated Learning Agents
The paper designs a federated learning incentive scheme that pays agents based on the total variation distance of their label distribution from an IID reference, and claims this induces high-quality participation.