This research explores Time-Sensitive Networking (TSN) and improved scheduling of TT and AVB traffic using the ITWA-SP approach. Visit: networkscience.researchw.com For Enquiries: contactus@researchw.com Award Nomination: networkscience-conferences.researchw.com/award-nomination/?ecategory=Awards&rcategory=Awardee #networking,#cybersecurity,#LAN,#WAN,#firewall,#IPaddressing,#VPN,#cloudnetwork,#TCPIP,#DNS,#datasecurity,#bandwidth,#routers,#switches,#subnetting,#networkengineer,#networkinfrastructure,#packetflow,#topology,#loadbalancing
Multi-Task Federated Split Learning Across Multi-Modal Data with Privacy Preservation With the advancement of federated learning (FL), there is a growing demand for schemes that support multi-task learning on multi-modal data while ensuring robust privacy protection, especially in applications like intelligent connected vehicles. Traditional FL schemes often struggle with the complexities introduced by multi-modal data and diverse task requirements, such as increased communication overhead and computational burdens. In this paper, we propose a novel privacy-preserving scheme for multi-task federated split learning across multi-modal data (MTFSLaMM). Our approach leverages the principles of split learning to partition models between clients and servers, employing a modular design that reduces computational demands on resource-constrained clients. To ensure data privacy, we integrate differential privacy to protect intermediate data and employ homomorphic encryption to safeguard client m...
Comments
Post a Comment