As Distributed Deep Learning (DDL) gains prominence, network constraints have emerged as a critical bottleneck impacting DDL performance. While state-of-the-art loss-tolerant (LT) transmission protocols enhance DDL efficiency, their application in ...

The emerging integration of IoT (Internet of Things) and AI (Artificial Intelligence) has unlocked numerous opportunities for innovation across diverse industries. However, growing privacy concerns and data isolation issues have inhibited this promising ...

Over the years, scientists have discovered bioactive chemicals in many of the plants that have been traditionally utilized as medicinal medicines. However, identifying plant species based on their physical characteristics can be difficult, and ...

Fault diagnosis in industrial robots is a critical aspect of intelligent manufacturing. However, the accuracy of fault diagnosis models can be significantly affected by the few-shot problem, which refers to the limited availability of labeled ...

Federated Learning is a collaborative machine learning paradigm that allows training models on decentralized data while preserving data privacy. It has gained significant attention due to its potential applications in various domains. However, ...

• FedCRMW: Robust watermarking for Federated Learning models.
• Novel trigger dataset construction scheme for watermarking.
• Enhanced robustness with feature-consistent training.

Federated Learning (FL) is a decentralized machine learning approach in which multiple participants collaboratively train a model. Participants keep data locally, train their local models, and aggregate them in a single global model in a ...

This research delves into the consequences of the high complexity of on-device operations executed during the federated learning process. We investigate how the varying computational capabilities and battery levels among mobile devices can ...

This research introduces a pioneering approach to address the intricate challenge of identifying kidney abnormalities in medical imaging. By synergizing the strengths of transfer learning and federated learning (FL), the study propels the ...

Federated Learning (FL) is a distributed approach to collaboratively training machine learning models. FL requires a high level of communication between the devices and a central server, thus imposing several challenges, including communication ...

Currently, Federated Learning is one of the most widely accepted distributed learning frameworks for privacy-sensitive applications. Despite the popularity gained, FL frameworks struggle to perform well in terms of accuracy and model convergence for ...

In recent years, the healthcare industry has witnessed a surge in the adoption of wearable devices, transforming how individuals manage their well-being. This paper explores the intersection of healthcare technology, data protection, and the ...

The privacy of personal data is paramount in the realm of assisted living and digital healthcare. Federated Learning (FL), with its decentralised model training approach, has emerged as a compelling solution to reconcile the need for personalised models ...

Microgrids face challenges in monitoring and controlling the power quality (PQ) of integrated electrical systems to make timely decisions. Inverter-based technologies handle small-scale smart grids’ power quality parameters (PQPs) and play an ...

Android devices are central to our daily lives, which leads to an increase in mobile security threats. Attackers try to exploit vulnerabilities and steal personal information from the installed applications on these devices. Because of their widespread ...

Federated Learning(FL) enables geographically dispersed organizations to collaboratively train a machine learning model. In this process, a parameter server enables global updating and synchronization of model by receiving and aggregating model data from ...

Software Engineering (SE) researchers are extensively applying Large Language Models (LLMs) to address challenges in SE tasks such as code clone detection, code summarization, and program comprehension. Despite promising results, LLMs have to be fine-...

In the realm of intelligent public transportation systems, deep learning (DL) techniques are widely used to extract valuable insights from mobility-related data, on top of which it is possible to realized several use-cases. However, since DL ...

Ensuring the data security and privacy stands as a prominent concern in the landscape of machine learning. The conventional approach of centralizing training data raises privacy concerns. Federated learning addresses this by avoiding the need to ...

As an emerging learning paradigm, Federated Learning (FL) enables data owners to collaborate training a model while keeps data locally. However, classic FL methods are susceptible to model poisoning attacks and Byzantine failures. Despite several defense ...

Autonomous robotics require secure and decentralized decision-making systems that ensure data privacy and computational efficiency, especially in critical areas. Current centralized models or human input are associated with data breaches and ...