Federated Learning: Architectures, Challenges, and Applications in Distribute Machine Learning Intelligence

Abstract

Federated Learning (FL) is a recently emerging machine learning model proposed to solve the issue of ownership, privacy, and security in collaborative model training. With FL, unlike the traditional centralized learning, raw data transfer to a central server is not required, and several devices or nodes can locally train a common model with their data. Only model updates are forwarded to a central server to be aggregated, and it keeps data privacy preserved and eliminates the potential of unauthorized access or leakage of data. The decentralized model is especially useful in sensitive sectors like health, finances, and agriculture where data privacy is the paramount issue. FL promotes the generalizability of the models because it uses various and distributed datasets, yet does not violate privacy laws. Nonetheless, it has some difficulties such as data heterogeneity, communication overhead and requirement of efficient security measures to maintain integrity of updates. With these drawbacks, federated learning shows great capabilities of facilitating privacy-conscious, collaborative artificial intelligence, and is a future outlook of distributed machine learning systems.