Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data
<|reference_start|>Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data: This extended abstract explores the integration of federated learning with deep transfer hashing for distributed prediction tasks, emphasizing resource-efficient client training from evolving data streams. Federated learning allows multiple clients to collaboratively train a shared model while maintaining data privacy - by incorporating deep transfer hashing, high-dimensional data can be converted into compact hash codes, reducing data transmission size and network loads. The proposed framework utilizes transfer learning, pre-training deep neural networks on a central server, and fine-tuning on clients to enhance model accuracy and adaptability. A selective hash code sharing mechanism using a privacy-preserving global memory bank further supports client fine-tuning. This approach addresses challenges in previous research by improving computational efficiency and scalability. Practical applications include Car2X event predictions, where a shared model is collectively trained to recognize traffic patterns, aiding in tasks such as traffic density assessment and accident detection. The research aims to develop a robust framework that combines federated learning, deep transfer hashing and transfer learning for efficient and secure downstream task execution.<|reference_end|>
@article{röder2024deep,
    title={Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data},
    author={Manuel R"oder and Frank-Michael Schleif},
    journal={arXiv preprint arXiv:2409.12575},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12575},
    primaryClass={cs.LG cs.CR cs.DC}
}