Summary of Mapl: Model Agnostic Peer-to-peer Learning, by Sayak Mukherjee et al.
MAPL: Model Agnostic Peer-to-peer Learning
by Sayak Mukherjee, Andrea Simonetto, Hadi Jamali-Rad
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel approach, Model Agnostic Peer-to-peer Learning (MAPL), simultaneously learns heterogeneous personalized models and a collaboration graph through peer-to-peer communication among neighboring clients. MAPL consists of two main modules: Personalized Model Learning (PML) and Collaborative Graph Learning (CGL). PML leverages contrastive losses for intra- and inter-client learning, while CGL dynamically refines collaboration weights based on local task similarities. Experimental results demonstrate the efficacy of MAPL, outperforming centralized model-agnostic counterparts without relying on central servers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers introduce a new way to help different devices learn together in a decentralized setting. They call it Model Agnostic Peer-to-peer Learning (MAPL). MAPL helps these devices learn personalized models and decide how much they should work together based on what tasks they’re doing. It’s like a team effort where each device contributes its own skills to achieve better results. The researchers tested MAPL and found that it works really well, often outperforming other methods without needing a central server. |
