federated learning

Rec-Def: A Recommendation-based Defence Mechanism for Privacy Preservation in Federated Learning Systems

November 19, 2023
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An emergence of attention and regulations on consumer privacy can be observed over the recent years with the ubiquitous availability of IoT systems handling personal data. Federated Learning (FL) arises as a privacy-preserved Machine Learning (ML) technique where data can be kept private within these devices without transmitting to third parties. Yet, many privacy attacks […]

Privacy-preserved Collaborative Federated Learning Platform for Industrial Internet of Things

November 19, 2023
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Federated learning (FL) is an intriguing approach to privacy-preserving collaborative learning. Decentralised FL is achieving increased favour for investigation due to the mitigation of vulnerability for a single point of failure and more controllability for end users over their models. However, many existing decentralised FL systems face limitations, such as privacy concerns, latency in aggregation, […]

FL-TIA: Novel Time Inference Attacks on Federated Learning

November 19, 2023
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Federated Learning (FL) is an emerging privacy-preserved distributed Machine Learning (ML) technique where multiple clients can contribute to training an ML model without sharing private data. Even though FL offers a certain level of privacy by design, recent works show that FL is vulnerable to numerous privacy attacks. One of the key features of FL […]

A Federated Learning Approach for Improving Security in Network Slicing

November 17, 2023
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Network Slicing (NS) is a predominant technology in future telecommunication networks, including Fifth Generation (5G), which supports the realization of heterogeneous applications and services. It allows the allocation of a dedicated logical network slice of the physical network to each application. Security is one of the paramount challenges in an NS ecosystem. Several technologies, including […]