Federated Learning for Privacy-Preserving Artificial Intelligence

Dear Colleagues,

This Special Issue aims to showcase recent advances in artificial intelligence and data privacy, focusing on how federated learning (FL) enables intelligent collaboration while preserving data confidentiality in distributed environments. With the rapid evolution of big data, cloud computing, and edge intelligence, privacy protection has become a critical requirement for trustworthy AI.

We invite original research and practical studies on algorithm design, system development, and cross-domain applications related to federated learning. Topics of interest include, but are not limited to, differential privacy and secure aggregation, modeling with heterogeneous and non-IID data, personalized and adaptive federated optimization, model compression and acceleration, federated inference, edge-cooperative computation, multi-agent systems, communication-efficient protocols, and privacy-aware large model training and deployment.

Applications in various domains such as healthcare, finance, transportation, manufacturing, education, and social computing are also highly encouraged. Through this Special Issue, we aim to foster theoretical innovation and practical implementation in privacy-preserving AI and promote the development of secure, transparent, and sustainable intelligent collaboration systems.

Prof. Dr. Chaoning Zhang
Guest Editor

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