A collision-only coordinated distributed algorithm for max-min fair multi-agent bandits achieves O(N^3 f(log T) log T) regret while preserving local reward privacy.
Expected time complexity of the auction algorithm and the push relabel algorithm for maximum bipartite matching on random graphs,
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Near-Optimal Privacy-Preserving Learning for Max-Min Fair Multi-Agent Bandits
A collision-only coordinated distributed algorithm for max-min fair multi-agent bandits achieves O(N^3 f(log T) log T) regret while preserving local reward privacy.