Summary of Gas: Generative Activation-aided Asynchronous Split Federated Learning, by Jiarong Yang and Yuan Liu
GAS: Generative Activation-Aided Asynchronous Split Federated Learning
by Jiarong Yang, Yuan Liu
First submitted to arxiv on: 2 Sep 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 paper proposes an asynchronous Split Federated Learning (SFL) framework, which addresses the issue of delayed updates caused by variations in client capabilities. The authors introduce an activation buffer and a model buffer on the server to manage asynchronous transmissions from clients. To mitigate biased updates due to resource-rich clients, they propose Generative activations-aided Asynchronous SFL (GAS), which maintains an activation distribution for each label and generates biased activations based on received distributions. These generative activations assist in updating the server-side model, ensuring more accurate convergence. The paper derives a tighter convergence bound and experimentally demonstrates the effectiveness of GAS. This study contributes to improving the performance of asynchronous SFL by introducing GAS, which can be applied to various applications, including natural language processing, computer vision, and recommender systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how we can make a type of machine learning work better when different computers have different strengths. Right now, some machines send information to the main server quickly, while others take longer. This makes it hard for the server to get accurate updates. To fix this problem, the authors created a new way for the servers to handle these updates. They use special buffers to keep track of the information and then adjust how the server updates its model based on what they receive. The paper shows that this new approach works better than previous methods and can be used in many different areas like language recognition, image analysis, and suggesting products. |
Keywords
» Artificial intelligence » Federated learning » Machine learning » Natural language processing
