  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.
