Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning
arXiv:2605.20975v1 Announce Type: new Abstract: Federated Learning enables collaborative model training across decentralized data sources without data transfer. Averaging-based FL is limited by the presence of non-IID data, which negatively impacts convergence speed and final model accuracy. Conventional alternatives suffer from significant inefficiency. Clients with noisy or highly heterogeneous data contribute expensive gradient computations that are either discarded or heavily down-weighted before aggregation. These reactive approaches waste computational resources, require more communicati
Emerging challenges in federated learning optimization, particularly with non-IID data, necessitate more efficient and robust client selection methods as FL deployments scale.
Improving the efficiency and fairness of federated learning can accelerate its adoption in privacy-sensitive sectors, enhancing collaborative AI development without centralizing data.
The focus shifts from reactive data handling in federated learning to proactive client selection, significantly reducing wasted computational resources and communication overhead.
- · Organizations with sensitive data
- · Federated learning platforms
- · AI-driven healthcare companies
- · Edge computing providers
- · Inefficient reactive FL systems
- · Centralized data processing models
More widespread and efficient deployment of federated learning across various industries.
Reduced data privacy concerns could accelerate the development of AI models in regulated sectors.
Increased decentralization of AI model training, potentially leading to a more resilient and distributed AI infrastructure.
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Read at arXiv cs.LG