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ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme
In this paper, we address the aforementioned limitations by introducing an On-Demand-FL, a client deployment approach for FL, offering more volume and heterogeneity of data in the learning process.
FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices
Our solution involves the design of: (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors such as accuracy and price, (2) intelligent matching algorithms that take into account the preferences of both parties in their design, and (3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the newly connected IoT devices.
A Federated Learning Scheme for Neuro-developmental Disorders: Multi-Aspect ASD Detection
Autism Spectrum Disorder (ASD) is a neuro-developmental syndrome resulting from alterations in the embryological brain before birth.
A Comparative Study of AI-based Intrusion Detection Techniques in Critical Infrastructures
Volunteer computing uses Internet-connected devices (laptops, PCs, smart devices, etc.
