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Found 7 papers, 1 papers with code
Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal
When conducting continual learning based on a publicly-released LLM checkpoint, the availability of the original training data may be non-existent.
Wasserstein Differential Privacy
We propose Wasserstein differential privacy (WDP), an alternative DP framework to measure the risk of privacy leakage, which satisfies the properties of symmetry and triangle inequality.
HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling for Long-Term Forecasting
Time series forecasting is crucial and challenging in the real world.
Federated Learning in Big Model Era: Domain-Specific Multimodal Large Models
Preliminary experiments show that enterprises can enhance and accumulate intelligent capabilities through multimodal model federated learning, thereby jointly creating an smart city model that provides high-quality intelligent services covering energy infrastructure safety, residential community security, and urban operation management.
The Prospect of Enhancing Large-Scale Heterogeneous Federated Learning with Transformers
Federated learning (FL) addresses data privacy concerns by enabling collaborative training of AI models across distributed data owners.
Hierarchical Federated Learning Incentivization for Gas Usage Estimation
Federated learning (FL) offers a solution to this problem by enabling local data processing on each participant, such as gas companies and heating stations.
Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout
Artificial intelligence (AI)-empowered industrial fault diagnostics is important in ensuring the safe operation of industrial applications.
