An age-of-information Markov scheduling policy that balances client selection intervals is claimed to speed up federated learning convergence by 7.5-20% over random selection.
Federated learning: Challenges, methods, and future directions
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Balancing Client Participation in Federated Learning Using AoI
An age-of-information Markov scheduling policy that balances client selection intervals is claimed to speed up federated learning convergence by 7.5-20% over random selection.