Non-Federated Multi-Task Split Learning for Heterogeneous Sources
Y Zheng, A Eryilmaz - arXiv preprint arXiv:2406.00150, 2024 - arxiv.org
With the development of edge networks and mobile computing, the need to serve
heterogeneous data sources at the network edge requires the design of new distributed …
heterogeneous data sources at the network edge requires the design of new distributed …
HSFL: an efficient split federated learning framework via Hierarchical Organization
Federated learning (FL) has emerged as a popular paradigm for distributed machine
learning among vast clients. Unfortunately, resource-constrained clients often fail to …
learning among vast clients. Unfortunately, resource-constrained clients often fail to …
Adaptsfl: Adaptive split federated learning in resource-constrained edge networks
The increasing complexity of deep neural networks poses significant barriers to
democratizing them to resource-limited edge devices. To address this challenge, split …
democratizing them to resource-limited edge devices. To address this challenge, split …
Adasplit: Adaptive trade-offs for resource-constrained distributed deep learning
Distributed deep learning frameworks like federated learning (FL) and its variants are
enabling personalized experiences across a wide range of web clients and mobile/IoT …
enabling personalized experiences across a wide range of web clients and mobile/IoT …
Federated Split Learning with Only Positive Labels for resource-constrained IoT environment
Distributed collaborative machine learning (DCML) is a promising method in the Internet of
Things (IoT) domain for training deep learning models, as data is distributed across multiple …
Things (IoT) domain for training deep learning models, as data is distributed across multiple …
Mergesfl: Split federated learning with feature merging and batch size regulation
Recently, federated learning (FL) has emerged as a popular technique for edge AI to mine
valuable knowledge in edge computing (EC) systems. To mitigate the computing …
valuable knowledge in edge computing (EC) systems. To mitigate the computing …
Federated Split Learning via Mutual Knowledge Distillation
L Luo, X Zhang - IEEE Transactions on Network Science and …, 2024 - ieeexplore.ieee.org
Federated learning (FL) and split learning (SL) can coordinate multiple clients (eg, end
devices in mobile/IoT networks) to collaboratively train deep learning models without …
devices in mobile/IoT networks) to collaboratively train deep learning models without …
FedSplitX: Federated Split Learning for Computationally-Constrained Heterogeneous Clients
Foundation models (FMs) have demonstrated remarkable performance in machine learning
but demand extensive training data and computational resources. Federated learning (FL) …
but demand extensive training data and computational resources. Federated learning (FL) …
Combined federated and split learning in edge computing for ubiquitous intelligence in internet of things: State-of-the-art and future directions
Federated learning (FL) and split learning (SL) are two emerging collaborative learning
methods that may greatly facilitate ubiquitous intelligence in the Internet of Things (IoT) …
methods that may greatly facilitate ubiquitous intelligence in the Internet of Things (IoT) …