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This paper proposes the first FTRL-type algorithms for decentralized online convex optimization with compressed communication, achieving elegant theoretical guarantees and improved regret bounds compared to previous OGD-type methods.
This paper compares contextual combinatorial bandits and policy gradient algorithms for decentralized smart charging of large EV fleets, using a realistic simulation with dynamic pricing and renewable energy data.