Aug 29, 2022 · In this paper, Democratic Federated Learning (DemoFL) is proposed, which is a privacy-preserving FL framework that has sufficiently low communication overheads.

Aug 29, 2022 · In this paper, we propose a communication-efficient, and privacy-preserving FL framework named DemoFL. It adopts MPC-based secret sharing methods.

We propose a new robust and privacy-preserving FL framework RoPPFL for edge computing applications, which supports hierarchical federated learning with privacy ...

Apr 1, 2024 · This paper proposes a novel secure and robust federated learning framework (SRFL) with trusted execution environments (TEEs).

Sep 14, 2023 · P2FLF strikes a balance between model accuracy and privacy protection while enabling efficient federated learning in mobile IoT environments.

Apr 17, 2024 · The framework provides security and robustness for federated learning on IoT devices under non-IID data by leveraging TEEs to safeguard ...

In this paper, we propose a robust privacy-preserving federated learning framework (PILE), which protects the privacy of local gradients and global models, ...

In this study, a blockchain based federated learning system using an enhanced weighted mean vector optimization algorithm, known as EINFO, is proposed.

This work combines secure multi-party computing and compressed sensing to propose a new chained federated learning scheme called Chain-FL via CS (Chain-FL-CS).

Jul 25, 2024 · Protecting FL data privacy on IoT devices is critical, and it requires robust defensive measures against threats such as inference attacks and ...