A novel semi-synchronous, decentralized, privacy-enhancing Federated Learning (FL) model built on Convolutional Neural Networks (CNNs) is put forth. The approach integrates federated learning with chaos-based encryption, utilizing the Henon Logistic Crossed Couple Map (HLCML) to strengthen the ...
McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence Statistics, pp. 1273–1282 (2017) Google Scholar Wu, G., Zhang, H., et al.: A decentralized approach for mining ev...
Federated Self-training for Semi-supervised Audio Recognition Federated Learning is a distributed machine learning paradigm dealing with decentralized and personal datasets. Since data reside on devices such as smartp... V Tsouvalas,A Saeed,T Ozcelebi - 《Acm Transactions on Embedded Computing Systems...
Pham Q-V, Leanh T, Tran NH, Park BJ, Hong CS (2018) Decentralized computation offloading and resource allocation for mobile-edge computing: a matching game approach. IEEE Access 6:75868–75885 Google Scholar Chen Z, Zhang J, Zheng X, Min G, Li J, Rong C (2024) Profit-aware coopera...
decentralized identifiers and verifiable credentials for machine learning scenarios involving sensitive data. Zepechnikov [5] developed privacy-preserving tools that can be used to ensure the learning process. Koti et al. [6] proposed an effective privacy-preserving machine learning framework which ...
Recently, federated learning (FL) has been proposed to train decentralized models in a privacy-preserved fashion depending on labeled data at the client-side, which is usually not available and costly. To address this, we propose \\verb!FedPerl!, a semi-supervised federated learning method. ...
In this work, we propose FedGAN, a Generative Adversarial Network (GAN) based federated learning method for semi-supervised image classification. In IoT scenarios, a big challenge is that decentralized data among multiple clients are normally non-independent and identically distributed (non-IID), ...
Federated semi-supervised learning (Fed-SSL) algorithms have been developed to address the challenges of decentralized data access, data confidentiality, and costly data labeling in distributed environments. Most existing Fed-SSL algorithms are based on the federated averaging approach, which utilizes an...
FL has become a promising solution for IDSs in IoT networks because it reduces the overhead in the learning process by engaging IoT devices during the training process. Three FL architectures are used to tackle the IDSs in IoT networks, including centralized (client–server), decentralized (...
Blockchain technology provides a decentralized, transparent, and tamper-proof foundation. Meanwhile, data islands have become a significant obstacle for machine learning applications. Although federated learning (FL) ensures data privacy protection, server-side security concerns persist. Traditional methods ...