Secure Aggregation for Federated Learning and Distributed Computation
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: 28 February 2026 | Viewed by 385
Special Issue Editors
Interests: information-theoretic security and privacy; secure aggregation; federated learning; private information retrieval (PIR); distributed computing
Special Issue Information
Dear Colleagues,
Federated learning (FL) enables collaborative model training across distributed clients while preserving data privacy. A core challenge in FL—and, more broadly, in distributed computation (DC)—is secure aggregation, which is ensuring that individual inputs, such as gradients and/or weights of deep neural network (DNN) models, remain confidential during the computation of global functions. While cryptographic methods offer practical solutions, information-theoretic approaches provide unconditional privacy guarantees, making them especially valuable for sensitive or adversarial environments.
Recent advances have applied Shannon-theoretic tools to characterize the fundamental limits of secure aggregation, design provably secure protocols, and analyze robustness under dynamic participation, partial trust, or collusion. These insights not only benefit FL but also inform the broader study of secure multi-party computation and privacy-preserving protocols in distributed learning systems.
Despite the progress, many open challenges remain. Key challenges include the tradeoff between communication and secret key rates, the impact of network topology on security and communication efficiency, and the applicability of information/coding-theoretic methods to adversarial environments. Bridging these gaps requires deeper integration between information theory, distributed optimization, and privacy-preserving machine learning.
This Special Issue welcomes original contributions that explore the use of information-theoretic tools and techniques to enhance the privacy, efficiency, and robustness of secure aggregation in federated learning and distributed computation. Topics of interest include, but are not limited to, the following:
- Fundamental limits and rate region characterizations;
- Protocol design with provable security and robustness guarantees;
- Key generation and coordination schemes;
- Secure aggregation over general network topologies;
- Information-theoretic analysis under realistic system constraints and attack models;
- Experimental validation of theory-inspired secure FL/DC protocols.
Dr. Xiang Zhang
Dr. Mingyue Ji
Guest Editors
Manuscript Submission Information
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Keywords
- federated learning
- secure aggregation
- distributed computing
- information-theoretic security
- privacy-preserving machine learning
- secret key generation
- communication efficiency
- linearly separable distributed computation
- robustness
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