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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.
FedDRL: A Trustworthy Federated Learning Model Fusion Method Based on Staged Reinforcement Learning
Traditional federated learning uses the number of samples to calculate the weights of each client model and uses this fixed weight value to fusion the global model.
Feature-context driven Federated Meta-Learning for Rare Disease Prediction
Additionally, a dynamic-weight based fusion strategy is proposed to further improve the accuracy of federated learning, which dynamically selects clients based on the accuracy of each local model.
