Summary of Stragglers-aware Low-latency Synchronous Federated Learning Via Layer-wise Model Updates, by Natalie Lang et al.
Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates
by Natalie Lang, Alejandro Cohen, Nir Shlezinger
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed straggler-aware layer-wise federated learning (SALF) algorithm addresses the challenge of limited computational resources and varying availability in synchronous federated learning. By leveraging backpropagation, SALF updates the global model in a layer-wise fashion, allowing stragglers to contribute partial gradients. This approach converges at the same rate as traditional FL without timing limitations. Experimental results demonstrate the performance gains of SALF over alternative mechanisms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Synchronous federated learning helps devices learn together. But sometimes, some devices have trouble keeping up and don’t finish training their models on time. This can slow down the whole process. The proposed method, straggler-aware layer-wise federated learning (SALF), lets these slower devices contribute to the overall model by providing partial updates. This helps everyone learn together faster. |
Keywords
* Artificial intelligence * Backpropagation * Federated learning
