Summary of Scala: Split Federated Learning with Concatenated Activations and Logit Adjustments, by Jiarong Yang and Yuan Liu
SCALA: Split Federated Learning with Concatenated Activations and Logit Adjustments
by Jiarong Yang, Yuan Liu
First submitted to arxiv on: 8 May 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 introduce Split Federated Learning (SFL) as a distributed machine learning framework that trains models collaboratively between a server and clients. However, data heterogeneity and client participation issues can lead to label distribution skew, hindering performance. To address this challenge, the authors propose SFL with Concatenated Activations and Logit Adjustments (SCALA), which combines activations from client-side models as input for the server-side model and adjusts logit functions to account for label distribution variations. Theoretical analysis and experiments demonstrate the effectiveness of SCALA on public datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SFL is a way to train machine learning models together with many devices or computers. But sometimes, the data and participation can be different, making it hard to get good results. To fix this, researchers came up with SFL with Concatenated Activations and Logit Adjustments (SCALA). SCALA takes the information from each device and combines it to help adjust for differences in data. This makes training better. The paper shows that SCALA works well on public datasets. |
Keywords
» Artificial intelligence » Federated learning » Machine learning
