MAPL: Model Agnostic Peer-to-peer Learning
<|reference_start|>MAPL: Model Agnostic Peer-to-peer Learning: 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<|reference_end|>
@article{mukherjee2024mapl:,
    title={MAPL: Model Agnostic Peer-to-peer Learning},
    author={Sayak Mukherjee, Andrea Simonetto, Hadi Jamali-Rad},
    journal={arXiv preprint arXiv:2403.19792},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19792},
    primaryClass={cs.LG cs.AI cs.CR cs.DC}
}