Federated learning(FL)is a novel technique in deep learning that enables clients to collaboratively train a shared model while retaining their decentralized data.However,researchers working on FL face several unique challenges,especially in the context of heterogeneity.Heterogeneity in data distributions,com...
In view of Mutual Privacy's emphasis on shared interests and common vulnerabilities that form the bedrock of our exist- ence, its formulation extends to both data protection as well as the larger sphere of autonomy driven account of information privacy. 2.1 Genetic The collective interest in...
Federated learningMutual informationWith the application of artificial intelligence in all field of life, people pay more attention to user privacy and data security. Under the condition of protecting user privacy, the federated learning model has become a popular research technology to solve the data...
Federated learning is a distributed machine learning algorithm that enables collaborative training among multiple clients without sharing sensitive information. Unlike centralized learning, it emphasizes the distinctive benefits of safeguarding data privacy. However, two challenging issues...
In this paper, we investigate the problem of decentralized federated learning (DFL) in Internet of things (IoT) systems, where a number of IoT clients train models collectively for a common task without sharing their private training data in the absence of a central server. Most of the existin...
Federated learning enables collaboratively training machine learning models on decentralized data. The three types of heterogeneous natures that is data, model, and objective bring about unique challenges to the canonical federated learning algorithm (FederatedAveraging), where one shared model is produced ...
INFORMATION sharingRESEARCH integrityINTERNET of thingsThis article, titled "Retracted: Mutual-Supervised Federated Learning and Blockchain-Based IoT Data Sharing," has been retracted by the publisher, Hindawi, due to evidence of systematic manipulation of the publication and peer-review p...
Decentralized Federated Learning via Mutual Knowledge Transferdoi:10.1109/JIOT.2021.3078543Chengxi LiGang LiPramod K. VarshneyIEEE
Federated Learning (FL), hailed as a potent approach in merging medical expertise, promises to elevate collaborative efforts among healthcare institutions while safeguarding the privacy and security of sensitive medical data, thereby energizing the trajectory of intelligent healthcare advancements. However,...
We give the detail design about the privacy preserving federated learning with mutual authentication, which provides the privacy-preserving and mutually verifiable federated learning framework on the vector space. To extend the numerical operations to the vector space, we modify the secret sharing of ...