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Found 3 papers, 1 papers with code
Federated Learning You May Communicate Less Often!
Moreover, specialized to the case $R=1$ (sometimes referred to as "one-shot" FL or distributed learning) our bounds suggest that the generalization error of the FL setting decreases faster than that of centralized learning by a factor of $\mathcal{O}(\sqrt{\log(K)/K})$, thereby generalizing recent findings in this direction to arbitrary loss functions and algorithms.
More Communication Does Not Result in Smaller Generalization Error in Federated Learning
We establish an upper bound on the generalization error that accounts explicitly for the effect of $R$ (in addition to the number of participating devices $K$ and dataset size $n$).
Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning
In this paper, we use tools from rate-distortion theory to establish new upper bounds on the generalization error of statistical distributed learning algorithms.
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