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Found 7 papers, 4 papers with code
R3HF: Reward Redistribution for Enhancing Reinforcement Learning from Human Feedback
Reinforcement learning from human feedback (RLHF) provides a paradigm for aligning large language models (LLMs) with human preferences.
Distributionally Generative Augmentation for Fair Facial Attribute Classification
This work proposes a novel, generation-based two-stage framework to train a fair FAC model on biased data without additional annotation.
Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI
However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed.
Unified Group Fairness on Federated Learning
We validate the advantages of the FMDA-M algorithm with various kinds of distribution shift settings in experiments, and the results show that FMDA-M algorithm outperforms the existing fair FL algorithms on unified group fairness.
Federated Unsupervised Representation Learning
FURL poses two new challenges: (1) data distribution shift (Non-IID distribution) among clients would make local models focus on different categories, leading to the inconsistency of representation spaces.
Federated Mutual Learning
The experiments show that FML can achieve better performance than alternatives in typical FL setting, and clients can be benefited from FML with different models and tasks.
Evaluation Framework For Large-scale Federated Learning
Federated learning is proposed as a machine learning setting to enable distributed edge devices, such as mobile phones, to collaboratively learn a shared prediction model while keeping all the training data on device, which can not only take full advantage of data distributed across millions of nodes to train a good model but also protect data privacy.
