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The paper introduces ZO-COSMO, an index-free method for decentralized zeroth-order optimization that improves accuracy by avoiding sparse index transmission through one-hop mixing. Experiments demonstrate gains over baseline methods on synthetic agents and Qwen LoRA workers.
This paper introduces Sheaf-based Federated Representation Learning (SFRL), a framework that aligns heterogeneous local representations via learnable sheaf restriction maps and a quadratic gluing regularizer, without assuming a shared global latent space. A decentralized algorithm (Sheaf-FRL) with convergence guarantees is proposed and shown to outperform baselines in cooperative classification under data and model heterogeneity.
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.