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Advances and Open Challenges in Federated Learning with Foundation Models
The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI), offering enhanced capabilities while addressing concerns of privacy, data decentralization, and computational efficiency.
STMGF: An Effective Spatial-Temporal Multi-Granularity Framework for Traffic Forecasting
Accurate Traffic Prediction is a challenging task in intelligent transportation due to the spatial-temporal aspects of road networks.
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.
Personalized Federated Learning for Multi-task Fault Diagnosis of Rotating Machinery
Intelligent fault diagnosis is essential to safe operation of machinery.
LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data
In this work, we propose a new approach, LSTMSPLIT, that uses SL architecture with an LSTM network to classify time-series data with multiple clients.
Two-Phase Multi-Party Computation Enabled Privacy-Preserving Federated Learning
Countries across the globe have been pushing strict regulations on the protection of personal or private data collected.
Distributed, Parallel, and Cluster Computing
Privacy-preserving Weighted Federated Learning within Oracle-Aided MPC Framework
In the second fold, an oracle-aided MPC solution for computing weighted federated learning is formalized by decoupling the security of federated learning systems from that of underlying multi-party computations.
Cryptography and Security
Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices
To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging the reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers' data.
