Decentralized learning in healthcare: a review of emerging techniques
Recent developments in deep learning have contributed to numerous success stories in
healthcare. The performance of a deep learning model generally improves with the size of …
healthcare. The performance of a deep learning model generally improves with the size of …
Split federated learning for 6G enabled-networks: Requirements, challenges and future directions
Sixth-generation (6G) networks anticipate intelligently supporting a wide range of smart
services and innovative applications. Such a context urges a heavy usage of Machine …
services and innovative applications. Such a context urges a heavy usage of Machine …
On Feasibility of Server-side Backdoor Attacks on Split Learning
Split learning is a collaborative learning design that allows several participants (clients) to
train a shared model while keeping their datasets private. In split learning, the network is …
train a shared model while keeping their datasets private. In split learning, the network is …
Can decentralized learning be more robust than federated learning?
Decentralized Learning (DL) is a peer--to--peer learning approach that allows a group of
users to jointly train a machine learning model. To ensure correctness, DL should be robust …
users to jointly train a machine learning model. To ensure correctness, DL should be robust …
Misa: Unveiling the vulnerabilities in split federated learning
Federated learning (FL) and split learning (SL) are prevailing distributed paradigms in
recent years. They both enable shared global model training while keeping data localized …
recent years. They both enable shared global model training while keeping data localized …
When minibatch sgd meets splitfed learning: Convergence analysis and performance evaluation
Federated learning (FL) enables collaborative model training across distributed clients (eg,
edge devices) without sharing raw data. Yet, FL can be computationally expensive as the …
edge devices) without sharing raw data. Yet, FL can be computationally expensive as the …
Incentivizing Participation in SplitFed Learning: Convergence Analysis and Model Versioning
In SplitFed learning (SFL), a global model is split into two segments, where distributed
clients train the first segment in a federated manner and a main server trains the other …
clients train the first segment in a federated manner and a main server trains the other …
Supvirus: A Scenario-Oriented Feature Poisoning Attack Approach in SplitFed Learning
Y Zhao, G Wu, J Hou, XY Li - 2023 19th International …, 2023 - ieeexplore.ieee.org
By combining the advantages of Federated Learning (FL) and Split Learning (SL), SplitFed
Learning (SFL) has become a widely applied scheme to train deep neural networks (DNNs) …
Learning (SFL) has become a widely applied scheme to train deep neural networks (DNNs) …
Enhancing efficiency and privacy in distributed machine learning: A comparative analysis of federated learning and split learning techniques
As the volume of data generated by individuals and organizations continues its exponential
grow, the need for efficient and secure Machine Learning (ML) algorithms has become …
grow, the need for efficient and secure Machine Learning (ML) algorithms has become …
Fortifying SplitFed Learning: Strengthening Resilience Against Malicious Clients
This article focuses on analyzing SplitFed Learning against model poisoning vulnerability
and developing methods to protect such a system against these attacks. SplitFed learning is …
and developing methods to protect such a system against these attacks. SplitFed learning is …