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Using fine results of DP theory, we have succeeded in establishing both privacy and utility guarantees, which show the superiority of DP-SCAFFOLD over the naive algorithm DP-FedAvg.
Apr 21, 2025 · We present the mathematical formulations that define the network model, ML model, energy consumption model, and communication model used to simulate the wireless …
Abstract—Currently, telecommunications companies seek to optimize wireless communication networks. This work proposes a practical method for classifying the coverage of the Long Term...
63. ABSTRACT Federated learning effectively addresses issues such as data privacy by collaborating across participating devices to train global models.
Apr 8, 2025 · This paper introduces a novel approach that integrates federated optimization, Extreme Learning Machine (ELM), and pure differential privacy, tailored specifically for edge …
Jun 16, 2025 · As the telecommunications industry continues to evolve, the demand for faster, more reliable networks has grown exponentially. Telecom companies are constantly looking for …
AutoDP-FL automates privacy budget (ε) tuning for federated learning. It uses Opacus for Differential Privacy (DP) in PyTorch, Flower for federated training, and popular architectures …
Jul 31, 2023 · As observed, a larger \ (\epsilon \) value of DP leads to more loss, particularly when the number of epochs is small. Additionally, the proposed framework suffers from less training …
Understanding and correctly applying composition theorems is fundamental for building federated learning systems with meaningful, quantifiable privacy guarantees over the entire training …
OctaiPipe has implemented Differential Privacy (DP) as a method of protecting sensitive data during federated training of neural network models. The most important parameter in DP is the …
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