3 articles tagged with #client-selection. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers propose EvoCSFL, a machine learning framework that optimizes client selection in federated learning systems by using surrogate models and evolutionary algorithms. The method balances model performance, communication latency, and energy consumption to achieve faster convergence and improved robustness compared to random selection approaches.
Researchers propose FedLECC, a new client selection strategy for federated learning that improves AI model training efficiency in distributed environments. The method groups clients by data similarity and prioritizes those with higher loss, achieving up to 12% better accuracy while reducing communication overhead by 50%.
Researchers propose a new client selection method for carbon-efficient federated learning that filters out noisy data to improve model performance. The approach uses gradient norm thresholding to better identify quality clients while maintaining sustainability goals in distributed AI training across renewable energy-powered data centers.
