Heterogeneous federated learning: State-of-the-art and research challenges
Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …
scale industrial applications. Existing FL works mainly focus on model homogeneous …
A systematic literature review on federated machine learning: From a software engineering perspective
Federated learning is an emerging machine learning paradigm where clients train models
locally and formulate a global model based on the local model updates. To identify the state …
locally and formulate a global model based on the local model updates. To identify the state …
Dispfl: Towards communication-efficient personalized federated learning via decentralized sparse training
Personalized federated learning is proposed to handle the data heterogeneity problem
amongst clients by learning dedicated tailored local models for each user. However, existing …
amongst clients by learning dedicated tailored local models for each user. However, existing …
Federated learning with cooperating devices: A consensus approach for massive IoT networks
Federated learning (FL) is emerging as a new paradigm to train machine learning (ML)
models in distributed systems. Rather than sharing and disclosing the training data set with …
models in distributed systems. Rather than sharing and disclosing the training data set with …
Decentralized federated averaging
Federated averaging (FedAvg) is a communication-efficient algorithm for distributed training
with an enormous number of clients. In FedAvg, clients keep their data locally for privacy …
with an enormous number of clients. In FedAvg, clients keep their data locally for privacy …
Topology-aware federated learning in edge computing: A comprehensive survey
The ultra-low latency requirements of 5G/6G applications and privacy constraints call for
distributed machine learning systems to be deployed at the edge. With its simple yet …
distributed machine learning systems to be deployed at the edge. With its simple yet …
Promptfl: Let federated participants cooperatively learn prompts instead of models-federated learning in age of foundation model
Quick global aggregation of effective distributed parameters is crucial to federated learning
(FL), which requires adequate bandwidth for parameters communication and sufficient user …
(FL), which requires adequate bandwidth for parameters communication and sufficient user …
Improving the model consistency of decentralized federated learning
To mitigate the privacy leakages and communication burdens of Federated Learning (FL),
decentralized FL (DFL) discards the central server and each client only communicates with …
decentralized FL (DFL) discards the central server and each client only communicates with …
Federated quantum machine learning
Distributed training across several quantum computers could significantly improve the
training time and if we could share the learned model, not the data, it could potentially …
training time and if we could share the learned model, not the data, it could potentially …
[HTML][HTML] Decentralized learning works: An empirical comparison of gossip learning and federated learning
Abstract Machine learning over distributed data stored by many clients has important
applications in use cases where data privacy is a key concern or central data storage is not …
applications in use cases where data privacy is a key concern or central data storage is not …