Federated learning is a privacy-preserving machine learning technique to train intelligent models from decentralized data, which enables exploiting private data by communicating local model updates in each iteration of model learning rather than the raw data. However, model updates can be extremely large...
Enhancing deep learning performance requires extensive datasets. Centralized training raises concerns about data ownership and security. Additionally, large models are often unsuitable for hospitals due to their limited resource capacities. Federated lea
Enhancing deep learning performance requires extensive datasets. Centralized training raises concerns about data ownership and security. Additionally, large models are often unsuitable for hospitals due to their limited resource capacities. Federated lea
Federated Learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model, without the need to share their local data. It is a promising solution for telemonitoring systems that demand intensive data collection, for detection, classification,...
Federated learning-aware multi-objective modeling and blockchain-enable system for IIoT applications. Comput Electr Eng. 2022;100:107839. Article Google Scholar Dong R, et al. Boosted kernel search: framework, analysis and case studies on the economic emission dispatch problem. Knowl-Based Syst....
《FedREDefense: Defending against Model Poisoning Attacks for Federated Learning using Model Update Reconstruction Error》(ICML 2024) GitHub: github.com/xyq7/FedREDefense [fig8]《Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learning》(CVPR 2024) GitHub: ...
In a context of sensitive user data, several works proposed privacy-preserving federated learning or closely related distributed learning that make use of differential privacy (Geyer et al. 2017; Shokri and Shmatikov 2015), cryptographic primitives (Bonawitz et al. 2016, 2017; Ryffel et al. 2020...
└── 📁GraphAnalysis#The implementation of the SML-based state-of-the-art└── 2PC_test.cpp#The unit tests for 2PC computations└── fed_gcn.cpp#The FedAvg-based federated learning approach└── graphsc_test.cpp#The SML-based state-of-the-art└── plaintext_gcn.cpp#Plaintext glob...
federated learning (FL) has become a popular machine learning paradigm for big data nowadays, which provides a potentially robust framework for automated perception and reasoning. Of course, if a good learning framework is to be built1, massive datasets should also be sufficiently trained to build...
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