Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligence

This research survey examines Federated Learning (FL), a distributed machine learning approach that enables collaborative AI model training without centralizing sensitive data. The paper covers FL's technical challenges, privacy mechanisms, and applications across healthcare, finance, and IoT systems.

• →Federated Learning enables collaborative AI training while preserving data privacy through decentralized architecture.
• →Key technical challenges include handling non-IID data distribution and reducing communication overhead between distributed nodes.
• →Privacy preservation mechanisms like differential privacy and secure aggregation are critical components of FL systems.
• →FL shows particular promise in regulated industries like healthcare and finance where data centralization is problematic.
• →Emerging trends include personalized FL and integration with quantum computing and reinforcement learning paradigms.