A survey on federated learning for resource-constrained IoT devices
Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …
model by learning from multiple decentralized edge clients. FL enables on-device training …
Federated machine learning: Survey, multi-level classification, desirable criteria and future directions in communication and networking systems
The communication and networking field is hungry for machine learning decision-making
solutions to replace the traditional model-driven approaches that proved to be not rich …
solutions to replace the traditional model-driven approaches that proved to be not rich …
Ties-merging: Resolving interference when merging models
Transfer learning–ie, further fine-tuning a pre-trained model on a downstream task–can
confer significant advantages, including improved downstream performance, faster …
confer significant advantages, including improved downstream performance, faster …
Data-free knowledge distillation for heterogeneous federated learning
Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global
server iteratively averages the model parameters of local users without accessing their data …
server iteratively averages the model parameters of local users without accessing their data …
Model-contrastive federated learning
Federated learning enables multiple parties to collaboratively train a machine learning
model without communicating their local data. A key challenge in federated learning is to …
model without communicating their local data. A key challenge in federated learning is to …
Federated learning from pre-trained models: A contrastive learning approach
Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to
learn collaboratively without sharing their private data. However, excessive computation and …
learn collaboratively without sharing their private data. However, excessive computation and …
Towards personalized federated learning
In parallel with the rapid adoption of artificial intelligence (AI) empowered by advances in AI
research, there has been growing awareness and concerns of data privacy. Recent …
research, there has been growing awareness and concerns of data privacy. Recent …
Federated learning on non-iid data silos: An experimental study
Due to the increasing privacy concerns and data regulations, training data have been
increasingly fragmented, forming distributed databases of multiple “data silos”(eg, within …
increasingly fragmented, forming distributed databases of multiple “data silos”(eg, within …