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Personalized Decentralized Federated Learning with Knowledge Distillation
To cope with this issue, we propose a personalized and fully decentralized FL algorithm, leveraging knowledge distillation techniques to empower each device so as to discern statistical distances between local models.
Asynchronous Decentralized Learning over Unreliable Wireless Networks
Decentralized learning enables edge users to collaboratively train models by exchanging information via device-to-device communication, yet prior works have been limited to wireless networks with fixed topologies and reliable workers.
Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup
This letter proposes a novel communication-efficient and privacy-preserving distributed machine learning framework, coined Mix2FLD.
Distilling On-Device Intelligence at the Network Edge
Devices at the edge of wireless networks are the last mile data sources for machine learning (ML).
Multi-hop Federated Private Data Augmentation with Sample Compression
On-device machine learning (ML) has brought about the accessibility to a tremendous amount of data from the users while keeping their local data private instead of storing it in a central entity.
Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data
On-device machine learning (ML) enables the training process to exploit a massive amount of user-generated private data samples.
