Summary of Towards Diverse Device Heterogeneous Federated Learning Via Task Arithmetic Knowledge Integration, by Mahdi Morafah et al.
Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration
by Mahdi Morafah, Vyacheslav Kungurtsev, Hojin Chang, Chen Chen, Bill Lin
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); 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 Federated Learning has emerged as a promising paradigm for collaborative machine learning, while preserving user data privacy. The paper addresses the limitations of standard Federated Learning by introducing TAKFL, a novel Knowledge Distillation (KD)-based framework that treats knowledge transfer from each device prototype’s ensemble as a separate task. This approach preserves the unique contributions and avoids dilution of informative logits from more capable devices. Additionally, TAKFL incorporates a KD-based self-regularization technique to mitigate issues related to the noisy and unsupervised ensemble distillation process. The framework also includes an adaptive task arithmetic knowledge integration process, allowing each student model to customize the knowledge integration for optimal performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning is a way for machines to learn together while keeping their data private. Right now, this kind of learning has limitations when it comes to devices that are very different from each other. Some devices are small and can only process simple information, while others are big and powerful. To solve this problem, the researchers created TAKFL, a new way for devices to share knowledge with each other. This approach makes sure that each device’s unique contributions are preserved and not mixed together. The method also helps fix problems that happen when devices don’t have enough data or are noisy. The results show that TAKFL works really well on various tasks and datasets. |
Keywords
» Artificial intelligence » Distillation » Federated learning » Knowledge distillation » Logits » Machine learning » Regularization » Student model » Unsupervised
