Analyzing the vulnerabilities in Split Federated Learning: assessing the robustness against data poisoning attacks
Sign Up to like & getrecommendations! Published in 2025 at "Scientific Reports"
DOI: 10.1038/s41598-025-15993-8
Abstract: Distributed Collaborative Machine Learning (DCML) offers a promising alternative to address privacy concerns in centralized machine learning. Split learning (SL) and Federated Learning (FL) are two effective learning approaches within DCML. Recently, there has been… read more here.
Keywords: data poisoning; split federated; attack; federated learning ... See more keywords
Adaptive Asynchronous Split Federated Learning for Medical Image Segmentation
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Access"
DOI: 10.1109/access.2024.3511430
Abstract: Split federated (SplitFed) learning offers promise for collaborative machine learning across decentralized and resource-constrained clients (edge devices, nodes, or organizations) in various applications, including healthcare. However, real-world challenges arise in heterogeneous environments where clients experience… read more here.
Keywords: split federated; segmentation; medical image; image segmentation ... See more keywords
Hierarchical Multiple Split Federated Learning for Low-Carbon Resource-Constrained User Equipment
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2024.3475637
Abstract: Split federated learning (SFL) allows clients with limited resources to engage in distributed machine learning, yet it grapples with issues related to energy usage and the efficiency of training. We propose low-carbon hierarchical multiple SFL… read more here.
Keywords: split federated; low carbon; federated learning; hierarchical multiple ... See more keywords
HeteroSFL: Split Federated Learning With Heterogeneous Clients and Non-IID Data
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2025.3572393
Abstract: Split Federated Learning (SFL) is an emerging privacy-preserving decentralized learning scheme which splits a machine learning model between client and server such that most of the computations are offloaded to the server. While SFL has… read more here.
Keywords: split federated; heterogeneous clients; non iid; federated learning ... See more keywords
Personalized Split Federated Learning With Early Exit: Pretraining and Online Learning Against Label Shifts
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2025.3601814
Abstract: Advancements in artificial intelligence (AI) have enabled Internet of Things (IoT) devices to offer intelligent services, improving system adaptability and scalability. split federated learning (SFL) has emerged as a promising approach for privacy-sensitive and resource-constrained… read more here.
Keywords: online learning; split federated; early exit; client ... See more keywords
Optimizing Privacy and Latency Tradeoffs in Split Federated Learning Over Wireless Networks
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Wireless Communications Letters"
DOI: 10.1109/lwc.2024.3471085
Abstract: In this letter, a novel cut layer selection scheme is designed to minimize the overall latency in split federated learning (SFL) over wireless networks, while maintaining an acceptable privacy level. Considering a tradeoff between overall… read more here.
Keywords: split federated; privacy; federated learning; layer selection ... See more keywords
Split Federated Learning Empowered Vehicular Edge Intelligence: Concept, Adaptive Design, and Future Directions
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Wireless Communications"
DOI: 10.1109/mwc.009.2400219
Abstract: To achieve ubiquitous intelligence in future vehicular networks, artificial intelligence (AI) is essential for extracting valuable insights from vehicular data to enhance AI-driven services. By integrating AI technologies into vehicular edge computing (VEC) platforms, which… read more here.
Keywords: split federated; vehicular edge; federated learning; intelligence ... See more keywords
Mitigating Model Poisoning and Tampering in Consumer IoT With HMAC in Split Federated Learning
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Consumer Electronics"
DOI: 10.1109/tce.2025.3603143
Abstract: The Consumer Internet of Things (CIoT) is rapidly transforming multiple aspects of daily life. To optimize routine operations, ensure the privacy of valuable consumer data, and enhance decision-making, CIoT devices require the adaptation of distributed… read more here.
Keywords: split federated; consumer; model poisoning; model ... See more keywords
Wireless Distributed Learning: A New Hybrid Split and Federated Learning Approach
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Wireless Communications"
DOI: 10.1109/twc.2022.3213411
Abstract: Cellular-connected unmanned aerial vehicle (UAV) with flexible deployment is foreseen to be a major part of the sixth generation (6G) networks. The UAVs connected to the base station (BS), as aerial users (UEs), could exploit… read more here.
Keywords: split; federated learning; split federated; distributed learning ... See more keywords
Enhancing Alzheimer’s disease classification through split federated learning and GANs for imbalanced datasets
Sign Up to like & getrecommendations! Published in 2024 at "PeerJ Computer Science"
DOI: 10.7717/peerj-cs.2459
Abstract: In the rapidly evolving healthcare sector, using advanced technologies to improve medical classification systems has become crucial for enhancing patient care, diagnosis, and treatment planning. There are two main challenges faced in this domain (i)… read more here.
Keywords: classification; alzheimer disease; split federated; imbalanced datasets ... See more keywords