IoT; machine learning; neural networks; federated learning; blockchain; learning on the edge1. Introduction The number of and use cases for IoT devices are increasing exponentially [1]; this is due in part to the increase in their ability to handle complex tasks that were once only possible ...
A Peer-to-Peer Payment System for Federated Learning Layered Architecture Data (Transaction, Block, ChainStructure, etc.) Network (Message, P2P, etc.) Consensus (Consensus, PoSap, etc.) Incentive Contract Application (Application, FedCoin, etc.) ...
Blockchain Other Benchmark, Dataset and Survey (27) Benchmark and Dataset (7) Survey (20) Statistical Challenges: distribution heterogeneity and label deficiency Distributed optimization Userful Federated Optimizer Baselines: FedAvg: Communication-Efficient Learning of Deep Networks from Decentralized Data. ...
Federated learning (FL) is a promising decentralized deep learning technology, which allows users to update models cooperatively without sharing their data
In recent years, with the rapid growth of edge data, the novel cloud-edge collaborative architecture has been proposed to compensate for the lack of data processing power of traditional cloud computing. On the other hand, on account of the increasin
BLOCKCHAINSELECTRICAL engineersTRUSTFederated learning (FL) represents a novel privacy‐preserving learning paradigm that offers a practical solution for distributed privacy preservation. Although privacy‐preserving FL based on homomorphic encryption (HE‐PPFL) exhibits resistance to gradient leakage ...
[172] could allow individual energy installations to autonomously update their local forecasting models over a blockchain network. Similarly, a system akin to MOCHA [173] could enable multiple energy sites to collaborate, aiming to enhance forecasting accuracy while ensuring data integrity. Research [...
Praneeth Vepakomma, Tristan Swedish, Ramesh Raskar, Otkrist Gupta, and Abhimanyu Dubey, "No Peek: A Survey of private distributed deep learning," Dec. 2018. Available:https://arxiv.org/abs/1812.03288,https://splitlearning.github.io/
Lu Y, Huang X, Zhang K, Maharjan S, Zhang Y (2020) Communication-efficient federated learning and permissioned blockchain for digital twin edge networks. IEEE Internet of Things Journal 8(4):2276–2288. Chen T, Jin X, Sun Y, Yin W (2020) Vafl: a method of vertical asynchronous federa...
Blockchain-Based Asynchronous Federated Learning (BAFL) code. Based on Hyperledger Fabric v2.3. If you think my code is useful, please cite my paper: @article{xu2022efficient, author={Xu, Chenhao and Qu, Youyang and Luan, Tom H. and Eklund, Peter W. and Xiang, Yong and Gao, Longxiang...