FL is a distributed machine learning paradigm that enables multiple parties to collaboratively train a model without sharing their individual data. Instead, each party trains a local model using their own data and shares the model updates with a central server, which aggregates them to improve the global model. This process preserves data privacy by avoiding the transfer of raw data between parties.
Recent Advances
Several recent advancements have empowered FL in biomedical research, including:
- Large-scale frameworks: Developments like Flower [7] and OpenFL [6] enable the training of large-scale models on distributed data, making it feasible to apply FL in big data settings.
- Privacy-preserving methods: Techniques like differential privacy [4, 12–14] and secure multi-party computation [30] help protect patient data during the training process.
- Integration with deep learning frameworks: Libraries like PyTorch [30] and TensorFlow [29] facilitate the integration of FL with state-of-the-art deep learning frameworks, making it easier to implement FL in biomedical research.
Challenges
Despite the promising advancements, several challenges persist in applying FL in biomedical research, including:
- Data heterogeneity: Biomedical data is often diverse and scattered across different sources, making it challenging to train accurate models without proper data integration techniques.
- Communication efficiency: In federated learning, communication efficiency is crucial to reduce the communication overhead between parties. However, in biomedical research, the number of parties involved can be large, leading to a significant increase in communication costs.
- Privacy risks: While privacy-preserving methods are essential for protecting patient data, they can also introduce additional complexity and computational overhead.
Conclusion
Federated learning has emerged as a promising approach to train machine learning models on distributed biomedical data while preserving patient privacy. Recent advancements in large-scale frameworks, privacy-preserving methods, and integration with deep learning frameworks have demystified the potential of FL in biomedical research. However, challenges like data heterogeneity, communication efficiency, and privacy risks must be addressed to fully harness the power of FL in this domain.
