Summary of Fedscalar: a Communication Efficient Federated Learning, by M. Rostami et al.
FedScalar: A Communication efficient Federated Learning
by M. Rostami, S. S. Kia
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 FedScalar, a novel federated learning (FL) algorithm that tackles the issue of high communication costs between agents and the central server in large-scale problems. Unlike traditional FL methods, which require agents to send high-dimensional vectors to the server, FedScalar enables agents to communicate updates using a single scalar. This is achieved by encoding model parameters into a scalar through an inner product with a random vector, followed by decoding at the server level. The proposed algorithm demonstrates a convergence rate of O(1/√K) for smooth, non-convex loss functions and reduces communication overhead. Numerical simulations validate its performance and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is like a big team project where many people work together on a problem without sharing their own information. But, it can be slow because everyone has to send lots of data to the leader. Researchers have created a new way called FedScalar that makes this process faster. Instead of sending lots of data, each person sends only one number that tells the leader what they did. The leader then uses these numbers to figure out what’s going on and make decisions. This new method is better because it saves time and energy. |
Keywords
» Artificial intelligence » Federated learning
