AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
This paper investigates a zeroth-order online optimization problem over a directed network, where each node can only access the function value of its local loss after receiving the decision variable, and information exchange among nodes may incur potential privacy leakage risks. To address these challenges, we propose...
The concept of Federated Learning (FL) allows training models in a decentralized way without distributing raw data but, nonetheless, the gradients are vulnerable to privacy attacks that include gradient inversion, reconstruction, and membership inference. Differential Privacy (DP) is broadly used to address these risks...
The recent growth in the privacy-sensitive artificial intelligence of distributed cloud-edge systems has accelerated the necessity of the implementation of efficient and thermally feasible encrypted inference engines. Fully Homomorphic Encryption (FHE) makes it possible to perform computation on encrypted data, and its...
The rapid growth of edge cloud infrastructure introduces new challenges in managing energy consumption data while ensuring privacy during energy trading. Existing studies focused on smart metering privacy, local electricity markets, and privacy-aware control, whereas privacy-preserving cloudlet energy trading with batt...
Federated learning (FL) enables collaborative model training across multiple clients in a privacy-preserving manner. However, the employment of homomorphic encryption algorithms might lead to high computational cost while the application of differential privacy (DP) methods would sacrifice model performance. To establi...
The proposed Secure Multiparty Computation protocol enables collaborative training of linear and logistic regression models while providing formal privacy guarantees for participant data, and adapts the iterative gradient descent algorithm to operate securely over secretly shared vectors.
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