10 papers analyzed
These studies suggest that federated learning improves machine learning efficiency and privacy, but faces challenges in security, optimization, and handling non-IID data.
Federated Learning (FL) is a decentralized machine learning approach that allows models to be trained across multiple devices or data centers without the need to centralize data. This method is particularly advantageous for privacy preservation and overcoming data silo issues. However, it introduces unique challenges in terms of optimization, security, and data heterogeneity.
Privacy and Security Concerns:
Optimization and Communication Efficiency:
Handling Non-IID Data:
Applications and Implementation:
Future Directions and Challenges:
Federated Learning offers a promising approach to decentralized, privacy-preserving machine learning. However, it faces significant challenges related to security, communication efficiency, and handling heterogeneous data. Ongoing research is focused on optimizing these aspects to make FL more robust and widely applicable. Future advancements in threat modeling, personalized techniques, and efficient communication strategies will be crucial for the broader adoption of FL.
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