Robust Optimization in Federated Learning for Industrial IoT: Mathematical Foundations
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: closed (10 January 2026) | Viewed by 2411
Special Issue Editors
Interests: distributed artificial intelligence; robust optimization; wireless sensor network
Special Issues, Collections and Topics in MDPI journals
Interests: intelligent E-Learning environments; virtual reality in education; AI in education
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
As the Industrial Internet of Things (IIoT) continues to reshape industrial landscapes, Federated Learning (FL) has emerged as a promising paradigm for collaborative machine learning across decentralized edge devices. However, the inherent challenges of dealing with uncertainties, security threats, and privacy concerns in IIoT environments necessitate robust optimization techniques. However, we can: explore novel mathematical models that enhance the robustness of FL in the face of uncertainties; develop mathematics solutions that integrate security measures into FL process, ensuring the integrity of model updates; introduce robust optimization techniques that minimize communication overhead in IIoT environments. This Special Issue aims to explore the mathematical foundations of robust optimization in FL for the Industrial IoT, providing a platform for researchers and practitioners to address critical issues and propel the field forward.
This Special Issue aims to advance the understanding and application of robust optimization in FL for the Industrial IoT through rigorous mathematical foundations. Your contributions are vital in shaping the future of intelligent and secure IIoT systems.
We invite contributions on, but not limited to, the following topics:
- Advanced mathematical models for robust optimization in Federated Learning;
- Innovative algorithms addressing uncertainties and dynamic conditions in the Industrial IoT;
- Privacy-preserving algorithms for secure collaboration in IIoT environments;
- Ensuring the integrity and authenticity of model updates in IIoT scenarios;
- Approaches for handling device heterogeneity in Federated Learning;
- Models and algorithms adapting to the dynamic nature of IIoT ecosystems;
- Applications of robust optimization in practical IIoT scenarios;
- Collaborative efforts between mathematicians, computer scientists, and industrial experts;
- Interdisciplinary research contributing to the advancement of FL in the Industrial IoT.
Dr. Chunjiong Zhang
Dr. Tao Xie
Guest Editors
Manuscript Submission Information
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Keywords
- robust optimization
- Federated Learning (FL)
- Industrial Internet of Things (IIoT)
- mathematical foundations
- communication-efficient optimization
- privacy-preserving techniques
- cybersecurity
- IIoT edge devices
- data privacy
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