conditionWang/FLNK
Federated Learning with New Knowledge -- explore to incorporate various new knowledge into existing FL systems and evolve these systems to reduce costs, extend their lifespan, and facilitate sustainable development.
This project helps researchers and practitioners in distributed machine learning overcome challenges when their data or tasks change over time while maintaining data privacy. It provides a comprehensive collection of research papers, datasets, and tools to integrate new information like evolving data features or new analytical goals into existing privacy-preserving learning systems. Data scientists, machine learning engineers, and research scientists working with sensitive data across multiple organizations would use this to keep their models relevant and efficient without compromising privacy.
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Use this if you are running a federated learning system and need to update your models with new data types, tasks, or more advanced algorithms without retraining from scratch or violating data privacy.
Not ideal if you are looking for a basic introduction to federated learning or if your data and tasks are static and do not evolve over time.
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Feb 07, 2024
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