Related papers: Federated Learning with Blockchain-Enhanced Machin…

Machine unlearning, which enables a model to forget specific data upon request, is increasingly relevant in the era of privacy-centric machine learning, particularly within federated learning (FL) environments. This paper presents a…

Federated learning (FL) enables multiple participants to collaboratively train machine learning models while ensuring their data remains private and secure. Blockchain technology further enhances FL by providing stronger security, a…

The widespread adoption of Internet of Things (IoT) devices in smart cities, intelligent healthcare systems, and various real-world applications have resulted in the generation of vast amounts of data, often analyzed using different Machine…

This paper proposes a blockchain-based Federated Learning (FL) framework with Intel Software Guard Extension (SGX)-based Trusted Execution Environment (TEE) to securely aggregate local models in Industrial Internet-of-Things (IIoTs). In FL,…

Federated learning (FL) is an effective paradigm for distributed environments such as the Internet of Things (IoT), where data from diverse devices with varying functionalities remains localized while contributing to a shared global model.…

Federated learning facilitates the collaborative training of models without the sharing of raw data. However, recent attacks demonstrate that simply maintaining data locality during training processes does not provide sufficient privacy…

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…

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…

As an emerging technique, Federated Learning (FL) can jointly train a global model with the data remaining locally, which effectively solves the problem of data privacy protection through the encryption mechanism. The clients train their…

In the expanding field of machine learning, federated learning has emerged as a pivotal methodology for distributed data environments, ensuring privacy while leveraging decentralized data sources. However, the heterogeneity of client data…

Federated learning (FL) enables collaborative training of a machine learning (ML) model across multiple parties, facilitating the preservation of users' and institutions' privacy by maintaining data stored locally. Instead of centralizing…

The advent of Federated Learning (FL) as a distributed machine learning paradigm has introduced new cybersecurity challenges, notably adversarial attacks that threaten model integrity and participant privacy. This study proposes an…

Context: Blockchain and AI are increasingly explored to enhance trustworthiness in software engineering (SE), particularly in supporting software evolution tasks. Method: We conducted a systematic literature review (SLR) using a predefined…

In recent years, the rapid integration of Internet of Things (IoT) devices into the healthcare sector has brought about revolutionary advancements in patient care and data management. While these technological innovations hold immense…

Artificial intelligence (AI) and deep learning techniques have gained significant attraction in recent years, owing to their remarkable capability of achieving high performance across a broad range of applications. However, a crucial…

Many studies have been done to improve the performance of centrally controlled business processes and enhance the integration between different parties of these collaborations. However, the most serious issues of collaborative business…

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…

Federated Learning (FL) in the Internet of Things (IoT) environments can enhance machine learning by utilising decentralised data, but at the same time, it might introduce significant privacy and security concerns due to the constrained…

Federated Learning (FL) is a decentralized machine learning (ML) paradigm in which models are trained on private data across several devices called clients and combined at a single node called an aggregator rather than aggregating the data…

Blockchain was always associated with Bitcoin, cryptocurrencies, and digital asset trading. However, its benefits are far beyond that. It supports technologies like the Internet-of-Things (IoT) to pave the way for futuristic smart…