Privacy-Preserving Decentralized Federated Learning via Explainable Adaptive Differential Privacy
This addresses privacy concerns for IoT devices in decentralized federated learning, offering an incremental improvement over existing differential privacy methods.
The paper tackles privacy risks in decentralized federated learning by proposing PrivateDFL, an explainable framework that combines hyperdimensional computing with differential privacy to track cumulative noise, resulting in higher accuracy, lower latency, and lower energy consumption compared to baselines, such as achieving 24.42% higher accuracy on MNIST under non-IID partitions.
Decentralized federated learning faces privacy risks because model updates can leak data through inference attacks and membership inference, a concern that grows over many client exchanges. Differential privacy offers principled protection by injecting calibrated noise so confidential information remains secure on resource-limited IoT devices. Yet without transparency, black-box training cannot track noise already injected by previous clients and rounds, which forces worst-case additions and harms accuracy. We propose PrivateDFL, an explainable framework that joins hyperdimensional computing with differential privacy and keeps an auditable account of cumulative noise so each client adds only the difference between the required noise and what has already been accumulated. We evaluate on MNIST, ISOLET, and UCI-HAR to span image, signal, and tabular modalities, and we benchmark against transformer-based and deep learning-based baselines trained centrally with Differentially Private Stochastic Gradient Descent (DP-SGD) and Renyi Differential Privacy (RDP). PrivateDFL delivers higher accuracy, lower latency, and lower energy across IID and non-IID partitions while preserving formal (epsilon, delta) guarantees and operating without a central server. For example, under non-IID partitions, PrivateDFL achieves 24.42% higher accuracy than the Vision Transformer on MNIST while using about 10x less training time, 76x lower inference latency, and 11x less energy, and on ISOLET it exceeds Transformer accuracy by more than 80% with roughly 10x less training time, 40x lower inference latency, and 36x less training energy. Future work will extend the explainable accounting to adversarial clients and adaptive topologies with heterogeneous privacy budgets.