To improve the security of the Internet of Medical Things (IoMT) in healthcare, this paper offers a Federated Learning (FL)-guided Intrusion Detection System (IDS) and an Artificial Neural Network (ANN)-based key exchange mechanism inside a blockchain framework. The IDS are essential for spotti...
The paper discusses communication cost reduction with sufficient privacy preservation. 2.1.3. FL Security Ali et al. [37] discussed the privacy challenges in the area of FL-based IoMT architectures. The privacy threats in these architectures were identified using deep reinforcement learning (DRL), ...
In addition, this paper proposes a time series-based method for device selection and computation offloading in the federated learning process, which selectively offloads the tasks of inefficient nodes to the edge computing center to reduce the training delay and energy consumption. Finally, experiments...
This can be challenging without federated learning, considering data privacy protection laws such as the EU’sGRPR, China’sPIPLand the recentEU AI Act, which prohibits cross-border data sharing. With federated learning, financial institutions can comply with these laws and regulations while using r...
Federated Learning is an effective solution to address the issues of data isolation and privacy leakage in machine learning. However, ensuring the security of network devices and architectures deploying federated learning remains a challenge due to netwo
作者: Seunghan Yang, Hyoungseob Park, Junyoung Byun, Changick Kim from KAIST Robust Federated Learning with Noisy Labels发表于2020 这篇文章符号写得有点混乱,介绍算法的部分有点乱。。。方法上有点像用…
However, the adversarial robustness of federated learning remains largely unexplored. This paper casts light on the challenge of adversarial robustness of federated learning. To facilitate a better understanding of the adversarial vulnerability of the existing FL methods, we conduct comprehensive robustness ...
Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitatio
Federated learning is a multiple device collaboration setup designed to solve machine learning problems under framework for aggregation and knowledge trans
Federated learning (FL) is a distributed machine learning (ML) framework. In FL, multiple clients collaborate to solve traditional distributed ML problems under the coordination of the central server without sharing their local private data with others. This paper mainly sorts out FLs based on machi...