Federated Learning in Edge Computing: A Privacy-Preserving Approach for AI Training
Abstract
With the proliferation of IoT and mobile devices, centralized AI training models face challenges related to data privacy, bandwidth consumption, and computational efficiency. Federated Learning (FL) offers a decentralized approach where AI models are trained across distributed edge devices without transmitting raw data. This paper examines FL’s role in enhancing privacy and reducing latency in AI training while addressing challenges such as model drift, communication overhead, and security vulnerabilities. Experimental results from real-world applications, including healthcare and smart cities, demonstrate FL’s effectiveness in preserving data confidentiality while maintaining high model accuracy.
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