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FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data
arXiv – CS AI|Daniel M. Jimenez-Gutierrez, Giovanni Giunta, Mehrdad Hassanzadeh, Aris Anagnostopoulos, Ioannis Chatzigiannakis, Andrea Vitaletti|
🤖AI Summary
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%.
Key Takeaways
- →FedLECC addresses client selection challenges in federated learning systems with non-IID data distributions.
- →The method clusters clients by label-distribution similarity and prioritizes those with higher local loss.
- →Test accuracy improvements of up to 12% were achieved compared to baseline methods.
- →Communication rounds were reduced by approximately 22% with overall communication overhead cut by up to 50%.
- →The approach enhances efficiency and scalability of federated learning in cloud-edge deployments.
#federated-learning#ai-optimization#distributed-computing#machine-learning#client-selection#cloud-edge#communication-efficiency
Read Original →via arXiv – CS AI
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