Summary of Fedtsa: a Cluster-based Two-stage Aggregation Method For Model-heterogeneous Federated Learning, by Boyu Fan et al.
FedTSA: A Cluster-based Two-Stage Aggregation Method for Model-heterogeneous Federated Learning
by Boyu Fan, Chenrui Wu, Xiang Su, Pan Hui
First submitted to arxiv on: 6 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers tackle the often-overlooked challenge of system heterogeneity in federated learning (FL), where clients have varying hardware resources that impact their training capacity. They propose FedTSA, a two-stage aggregation method tailored to this issue, which clusters clients based on their capabilities and aggregates models using conventional weight averaging for homogeneous models and deep mutual learning with a diffusion model for heterogeneous models. The authors demonstrate the effectiveness of FedTSA through extensive experiments, highlighting its potential as a promising approach for model-heterogeneous FL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way to train machines together without sharing their data. But what if each machine has different powers? That’s a problem that this paper tries to solve. The authors create a new way to combine models from these machines, called FedTSA. It works by grouping the machines into categories based on their power and then combining their models in two steps. This helps the machines work together better, even if they have different powers. |
Keywords
* Artificial intelligence * Diffusion model * Federated learning
