Summary of Fedsikd: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated Learning, by Yousef Alsenani et al.
FedSiKD: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated Learning
by Yousef Alsenani, Rahul Mishra, Khaled R. Ahmed, Atta Ur Rahman
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 This paper introduces FedSiKD, a novel approach to federated learning that incorporates knowledge distillation within a similarity-based framework. The proposed method addresses the challenges of training machine learning models in a decentralized manner while preserving data privacy, particularly when dealing with non-i.i.d. client data and device constraints. By securely sharing statistics about their data distribution, clients promote intra-cluster homogeneity, enhancing optimization efficiency and accelerating the learning process. FedSiKD outperforms state-of-the-art algorithms on various datasets, including HAR and MNIST, achieving higher accuracy and faster convergence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way for different devices to work together without sharing their data. This helps keep our information private. The problem is that the devices might have different kinds of data, which makes it hard for them to learn together. To solve this, researchers created FedSiKD. It’s like a teacher and student working together. The teacher shares its knowledge with the student, making learning faster and more accurate. This new method is better than others at doing this, even when there are lots of devices involved. |
Keywords
* Artificial intelligence * Federated learning * Knowledge distillation * Machine learning * Optimization
