A5029803129- Privacy-Preserving Technologies in Data
- Advanced Multi-Objective Optimization Algorithms
- Speech and Audio Processing
- Speech Recognition and Synthesis
- Tropical and Extratropical Cyclones Research
- Evolutionary Algorithms and Applications
- Mobile Crowdsensing and Crowdsourcing
- Cryptography and Data Security
- Internet Traffic Analysis and Secure E-voting
- Music and Audio Processing
- Metaheuristic Optimization Algorithms Research
University of Southern California
2023-2025
Over the past few years, Federated Learning (FL) has become an emerging machine learning technique to tackle data privacy challenges through collaborative training. In algorithm, clients submit a locally trained model, and server aggregates these parameters until convergence. Despite significant efforts that have been made FL in fields like computer vision, audio, natural language processing, applications utilizing multimodal streams remain largely unexplored. It is known broad real-world...
In Federated Learning (FL), clients may have weak devices that cannot train the full model or even hold it in their memory space. To implement large-scale FL applications, thus, is crucial to develop a distributed learning method enables participation of such clients. We propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EmbracingFL</monospace> , general framework allows all available join training regardless system resource capacity....