Summary of Rate-constrained Quantization For Communication-efficient Federated Learning, by Shayan Mohajer Hamidi et al.
Rate-Constrained Quantization for Communication-Efficient Federated Learning
by Shayan Mohajer Hamidi, Ali Bereyhi
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 Rate-Constrained Federated Learning (RC-FED), a novel framework that optimizes both fidelity and data rate constraints in federated learning. The authors recognize that existing quantization methods for federated learning are limited by their inability to balance distortion and communication cost. To address this, they formulate RC-FED as a joint optimization problem that minimizes quantization distortion while keeping the rate of encoded gradients below a target threshold. This allows for a tunable trade-off between distortion and communication cost. The paper analyzes the convergence behavior of RC-FED and demonstrates its superior performance on several datasets compared to baseline quantized FL schemes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way for many devices to work together to learn from data without sharing it directly. One problem with this approach is that it uses up a lot of bandwidth, which can be expensive. To solve this, the authors developed a new method called RC-FED. It’s like a balance beam where you need to make sure not too much information gets lost (distortion) but also not too much data gets used up (bandwidth). The authors show that their method works better than other methods on different types of datasets. |
Keywords
» Artificial intelligence » Federated learning » Optimization » Quantization
