Summary of Multi-level Additive Modeling For Structured Non-iid Federated Learning, by Shutong Chen et al.
Multi-Level Additive Modeling for Structured Non-IID Federated Learning
by Shutong Chen, Tianyi Zhou, Guodong Long, Jie Ma, Jing Jiang, Chengqi Zhang
First submitted to arxiv on: 26 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed Multi-level Additive Models (MAM) framework, called Federated MAM (FeMAM), addresses the primary challenge in Federated Learning by capturing and exploiting the fine-grained structure of non-IID distributions across clients. FeMAM organizes models in a multi-level hierarchy to improve knowledge-sharing across heterogeneous clients and their personalization. Each client is assigned to at most one model per level, with personalized predictions summing up outputs from multiple models. The top-level global model uses FedAvg, while mid-level models are trained for subgroups of clients, and bottom-level models are tailored for individual clients. To approximate non-IID structures, FeMAM introduces flexibility by incrementally adding new models and reassigning others as needed, optimizing knowledge-sharing. Extensive experiments demonstrate FeMAM’s superiority over existing methods in various settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning helps devices learn together without sharing data. The problem is that each device might have different information, making it hard to share knowledge. To fix this, researchers created a new way of organizing models called Multi-level Additive Models (MAM). This framework, Federated MAM (FeMAM), helps devices learn from each other better by creating multiple levels of models that work together. Each device is assigned to at most one model per level, and the predictions are combined to make better decisions. FeMAM even adapts to changing information needs by adding or reassigning models as needed. Tests show that FeMAM works well in different situations. |
Keywords
» Artificial intelligence » Federated learning
