Related papers: Blockchain Assisted Decentralized Federated Learni…
In this paper, we study a new latency optimization problem for blockchain-based federated learning (BFL) in multi-server edge computing. In this system model, distributed mobile devices (MDs) communicate with a set of edge servers (ESs) to…
In Industry 4.0 systems, a considerable number of resource-constrained Industrial Internet of Things (IIoT) devices engage in frequent data interactions due to the necessity for model training, which gives rise to concerns pertaining to…
The blockchain technology has been extensively studied to enable distributed and tamper-proof data processing in federated learning (FL). Most existing blockchain assisted FL (BFL) frameworks have employed a third-party blockchain network…
In the era of deep learning, federated learning (FL) presents a promising approach that allows multi-institutional data owners, or clients, to collaboratively train machine learning models without compromising data privacy. However, most…
Federated Learning (FL) is a privacy-preserving distributed machine learning scheme, where each participant data remains on the participating devices and only the local model generated utilizing the local computational power is transmitted…
Federated learning (FL), thanks in part to the emergence of the edge computing paradigm, is expected to enable true real-time applications in production environments. However, its original dependence on a central server for orchestration…
Despite the great potential of Federated Learning (FL) in large-scale distributed learning, the current system is still subject to several privacy issues due to the fact that local models trained by clients are exposed to the central…
Federated Learning (FL) is a machine learning method for training with private data locally stored in distributed machines without gathering them into one place for central learning. Despite its promises, FL is prone to critical security…
Federated Learning (FL) has emerged as a key paradigm for building Trustworthy AI systems by enabling privacy-preserving, decentralized model training. However, FL is highly susceptible to adversarial attacks that compromise model integrity…
Federated learning(FL) is a rapidly growing field and many centralized and decentralized FL frameworks have been proposed. However, it is of great challenge for current FL frameworks to improve communication performance and maintain the…
Federated machine learning (FL) allows to collectively train models on sensitive data as only the clients' models and not their training data need to be shared. However, despite the attention that research on FL has drawn, the concept still…
Federated learning (FL) enables collaborative training of a shared model on edge devices while maintaining data privacy. FL is effective when dealing with independent and identically distributed (iid) datasets, but struggles with non-iid…
Home appliance manufacturers strive to obtain feedback from users to improve their products and services to build a smart home system. To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging…
The metaverse, emerging as a revolutionary platform for social and economic activities, provides various virtual services while posing security and privacy challenges. Wearable devices serve as bridges between the real world and the…
Federated Learning (FL) facilitates collaborative model training across distributed clients while ensuring data privacy. Traditionally, FL relies on a centralized server to coordinate learning, which creates bottlenecks and a single point…
Federated Learning (FL) is a distributed, and decentralized machine learning protocol. By executing FL, a set of agents can jointly train a model without sharing their datasets with each other, or a third-party. This makes FL particularly…
Federated learning is a decentralized machine learning paradigm that allows multiple clients to collaborate by leveraging local computational power and the models transmission. This method reduces the costs and privacy concerns associated…
Many researchers have proposed replacing the aggregation server in federated learning with a blockchain system to improve privacy, robustness, and scalability. In this approach, clients would upload their updated models to the blockchain…
Edge computing brings a new paradigm in which the sharing of computing, storage, and bandwidth resources as close as possible to the mobile devices or sensors generating a large amount of data. A parallel trend is the rise of phones and…
Blockchain promises to enhance distributed machine learning (ML) approaches such as federated learning (FL) by providing further decentralization, security, immutability, and trust, which are key properties for enabling collaborative…