In these frameworks, parties need to merge their local model parameters into a global model, and Homomorphic Encryption ensures the completion of this process without revealing individual participants' model information. Park et al. [31] presented a Homomorphic Encryption-based federated learning scheme...
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...
… I say. The portfolio will be constructed by “a proprietary, quantitative and artificial intelligence driven model [that] utilizes machine learning to analyze constantly evolving financial markets data and to identify and recall patterns in markets. Based on those patterns, the Model dynamically al...
For the sake of those readers in the advisor community, I asked Justin to share some information about their new discussion community. Here’s his description> [We] recently launched APViewpoint, a secure discussion forum and “online study group.” APViewpoint enables investment advisers, ...
Federated learning (FL) enables collaboratively training deep learning models on decentralized data. However, there are three types of heterogeneities in FL setting bringing about distinctive challenges to the
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,...
Decentralized Federated Learning via Mutual Knowledge Transferdoi:10.1109/JIOT.2021.3078543Chengxi LiGang LiPramod K. VarshneyIEEE
Deep mutual learningNon-independent and identical distribution (Non-IID) data and model heterogeneity pose a great challenge for federated learning in cloud-based and edge-based systems. They are easy to lead to inconsistency of gradient updates during the training stage and mismatch of gradient ...
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 ...