Summary of Fedcrl: Personalized Federated Learning with Contrastive Shared Representations For Label Heterogeneity in Non-iid Data, by Chenghao Huang et al.
FedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data
by Chenghao Huang, Xiaolu Chen, Yanru Zhang, Hao Wang
First submitted to arxiv on: 27 Apr 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 The proposed Federated Contrastive Shareable Representations (FedCoSR) algorithm addresses issues of heterogeneity in intelligent communication applications. By sharing local model parameters, typical representations, and engaging in contrastive learning, FedCoSR facilitates knowledge sharing while maintaining data privacy. The algorithm also ensures fairness for clients with scarce data through adaptive local aggregation. Simulation results demonstrate improved accuracy and fairness over existing methods on datasets with varying label heterogeneity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem in artificial intelligence. It makes it possible for different devices to work together without sharing their personal data. This is important because some devices might not have enough information or the information they do have might be biased. The new algorithm, called FedCoSR, helps devices share what they know while keeping their data private. It also makes sure that all devices are treated fairly, even if one device doesn’t have as much information as others. |
