Federated Knowledge Recycling: Privacy-Preserving Synthetic Data Sharing
It addresses privacy and security issues in federated learning for institutions, though it appears incremental as it builds on existing data generation and aggregation methods.
The paper tackles privacy vulnerabilities in federated learning by introducing Federated Knowledge Recycling (FedKR), which uses locally generated synthetic data for collaboration, achieving an average 4.24% accuracy improvement over local training.
Federated learning has emerged as a paradigm for collaborative learning, enabling the development of robust models without the need to centralise sensitive data. However, conventional federated learning techniques have privacy and security vulnerabilities due to the exposure of models, parameters or updates, which can be exploited as an attack surface. This paper presents Federated Knowledge Recycling (FedKR), a cross-silo federated learning approach that uses locally generated synthetic data to facilitate collaboration between institutions. FedKR combines advanced data generation techniques with a dynamic aggregation process to provide greater security against privacy attacks than existing methods, significantly reducing the attack surface. Experimental results on generic and medical datasets show that FedKR achieves competitive performance, with an average improvement in accuracy of 4.24% compared to training models from local data, demonstrating particular effectiveness in data scarcity scenarios.