Summary of Beyond the Federation: Topology-aware Federated Learning For Generalization to Unseen Clients, by Mengmeng Ma and Tang Li and Xi Peng
Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients
by Mengmeng Ma, Tang Li, Xi Peng
First submitted to arxiv on: 6 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 proposed Topology-aware Federated Learning (TFL) method addresses the challenge of out-of-federation (OOF) generalization in federated learning, leveraging client topology to train robust models against OOF data. The approach consists of two modules: Client Topology Learning and Learning on Client Topology. TFL is shown to achieve superior OOF robustness and scalability on various real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning helps with sensitive data from different places, but it gets worse when new people are added or things change. People tried to fix this, but they didn’t do well because it’s too hard to share information. To make it better, we created a way called Topology-aware Federated Learning (TFL). TFL looks at how the different groups relate to each other and uses that to train models that work well even when new people are added. |
Keywords
» Artificial intelligence » Federated learning » Generalization
