Summary of Online-score-aided Federated Learning: Taming the Resource Constraints in Wireless Networks, by Md Ferdous Pervej and Minseok Choi and Andreas F. Molisch
Online-Score-Aided Federated Learning: Taming the Resource Constraints in Wireless Networks
by Md Ferdous Pervej, Minseok Choi, Andreas F. Molisch
First submitted to arxiv on: 12 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Networking and Internet Architecture (cs.NI); Systems and Control (eess.SY)
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 In this research paper, a new federated learning (FL) algorithm called OSAFL is proposed to address challenges in wireless network settings. The authors acknowledge that limited radio and computational resources are significant limitations in FL applications. To overcome these limitations, the proposed algorithm leverages normalized gradient similarities and optimized client weighting to facilitate convergence rate improvement. The algorithm is designed for tasks relevant to wireless applications under practical considerations such as storage constraints and online sample arrival. Simulation results on two different tasks with three datasets and four popular ML models demonstrate the effectiveness of OSAFL compared to six existing FL baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way to learn from many devices without sharing their data. But, wireless networks have some big challenges like limited storage and new data arriving online. The authors propose a new algorithm called OSAFL that helps with these challenges. It uses special techniques to make sure the algorithm works well even when devices don’t have much resources. The results show that OSAFL is better than other algorithms in this area. |
Keywords
» Artificial intelligence » Federated learning
