Derives unique closed-form decentralized policy minimizing worst-agent online regret that asymptotically converges to centralized Nash-optimal policy in mean-field limit, with added online mixture weighting.
Joint local relational aug- mentation and global Nash equilibrium for federated learning with non-iid data
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MEMOA: Massive Mixtures of Online Agents via Mean-Field Decentralized Nash Equilibria
Derives unique closed-form decentralized policy minimizing worst-agent online regret that asymptotically converges to centralized Nash-optimal policy in mean-field limit, with added online mixture weighting.