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Found 2 papers, 1 papers with code
Byzantine-Resilient Federated Learning at Edge
To address this issue, we study the stochastic convex and non-convex optimization problem for federated learning at edge and show how to handle heavy-tailed data while retaining the Byzantine resilience, communication efficiency and the optimal statistical error rates simultaneously.
Incentive-Aware Recommender Systems in Two-Sided Markets
Our framework models this incentive-aware system as a multi-agent bandit problem in two-sided markets, where the interactions of agents and arms are facilitated by recommender systems on online platforms.
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