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Found 7 papers, 1 papers with code
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.
Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning
Traditional distributed deep reinforcement learning (RL) commonly relies on exchanging the experience replay memory (RM) of each agent.
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.
Federated Reinforcement Distillation with Proxy Experience Memory
In distributed reinforcement learning, it is common to exchange the experience memory of each agent and thereby collectively train their local models.
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.
Blockchained On-Device Federated Learning
By leveraging blockchain, this letter proposes a blockchained federated learning (BlockFL) architecture where local learning model updates are exchanged and verified.
Information Theory Networking and Internet Architecture Information Theory
