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Found 7 papers, 4 papers with code
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.
Defending Backdoor Data Poisoning Attacks by Using Noisy Label Defense Algorithm
We first discuss the limitations of directly using the noisy-label defense algorithms to defend against backdoor attacks.
Federated Adversarial Debiasing for Fair and Transferable Representations
While adversarial learning is commonly used in centralized learning for mitigating bias, there are significant barriers when extending it to the federated framework.
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.
Off-Policy Imitation Learning from Observations
To further accelerate the learning procedure, we regulate the policy update with an inverse action model, which assists distribution matching from the perspective of mode-covering.
Transfer Learning in Deep Reinforcement Learning: A Survey
Reinforcement learning is a learning paradigm for solving sequential decision-making problems.
Learning Sparse Rewarded Tasks from Sub-Optimal Demonstrations
SAIL bridges the advantages of IL and RL to reduce the sample complexity substantially, by effectively exploiting sup-optimal demonstrations and efficiently exploring the environment to surpass the demonstrated performance.
