MAPL: Model Agnostic Peer-to-peer Learning

• URL: http://arxiv.org/abs/2403.19792v1
• Date: Thu, 28 Mar 2024 19:17:54 GMT
• Title: MAPL: Model Agnostic Peer-to-peer Learning
• Authors: Sayak Mukherjee, Andrea Simonetto, Hadi Jamali-Rad,
• Abstract summary: We introduce Model Agnostic Peer-to-peer Learning (MAPL) to simultaneously learn heterogeneous personalized models and a collaboration graph. 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 decentralized Collaborative Graph Learning (CGL) dynamically refining collaboration weights based on local task similarities.
• Score: 2.9221371172659616
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 decentralized Collaborative Graph Learning (CGL) dynamically refining collaboration weights in a privacy-preserving manner based on local task similarities. Our extensive experimentation demonstrates the efficacy of MAPL and its competitive (or, in most cases, superior) performance compared to its centralized model-agnostic counterparts, without relying on any central server. Our code is available and can be accessed here: https://github.com/SayakMukherjee/MAPL

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