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MAPL: Model Agnostic Peer-to-peer Learning
April 1, 2024, 4:41 a.m. | Sayak Mukherjee, Andrea Simonetto, Hadi Jamali-Rad
cs.LG updates on arXiv.org arxiv.org
Abstract: Effective collaboration among heterogeneous clients in a decentralized setting is a rather unexplored avenue in the literature. To structurally address this, we introduce Model Agnostic Peer-to-peer Learning (coined as MAPL) a novel approach to simultaneously learn heterogeneous personalized models as well as a collaboration graph through peer-to-peer communication among neighboring clients. MAPL is comprised of two main modules: (i) local-level Personalized Model Learning (PML), leveraging a combination of intra- and inter-client contrastive losses; (ii) network-wide …
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