Context-Aware Federated Learning with Differential Privacy
Abstract
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL models often operate under the assumption of homogeneous data distributions, which frequently doesn't hold true in real-world scenarios. This research investigates a novel framework for context-aware federated learning that addresses this limitation by integrating local contextual information alongside differential privacy mechanisms. The core claim is that incorporating context allows for more accurate model aggregation, while differential privacy safeguards user data. The proposed system designs a federated learning architecture that dynamically adjusts model updates based on device-specific context and employs differential privacy to mitigate privacy risks. We demonstrate the potential of this combined approach to achieve higher model accuracy and enhanced privacy protection compared to standard FL methods. This work contributes to a more robust and practical distributed learning paradigm suitable for diverse and heterogeneous data environments.
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