An information-theoretic upper bound on a Markov-chain-weighted generalization error for federated learning under concept drift, together with a regularized ERM algorithm and a Pareto cost-performance analysis.
Advances and open problems in federated learning,
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An Information-Theoretic Analysis for Federated Learning under Concept Drift
An information-theoretic upper bound on a Markov-chain-weighted generalization error for federated learning under concept drift, together with a regularized ERM algorithm and a Pareto cost-performance analysis.