Summary of Communication-efficient and Privacy-preserving Decentralized Meta-learning, by Hansi Yang et al.
Communication-Efficient and Privacy-Preserving Decentralized Meta-Learning
by Hansi Yang, James T. Kwok
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); 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 This paper proposes a decentralized algorithm called LoDMeta (Local Decentralized Meta-learning) for meta-learning in the big-data era. The existing distributed learning algorithms assume all clients share the same task, but this approach considers the more challenging setting where different clients perform different tasks with limited training data. LoDMeta uses local auxiliary optimization parameters and random perturbations on the model parameter to reduce communication cost and protect privacy. Theoretical results are provided for both convergence and privacy analysis, demonstrating that LoDMeta achieves similar meta-learning accuracy as centralized algorithms while not requiring data gathering from each client and protecting data privacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LoDMeta is a new way of doing machine learning without storing all the data in one place. It’s like having multiple people learn together, but each person has their own special task to work on. This makes it more efficient and private, which means our personal information stays safe. The researchers created LoDMeta by adding some extra steps to regular decentralized algorithms. They tested it with different types of data and showed that it works just as well as when all the data is stored in one place. |
Keywords
» Artificial intelligence » Machine learning » Meta learning » Optimization
