Description:
- Federated learning
- Healthcare data
- Financial data
Abstract
USC researchers have developed LightSecAgg, a novel approach for secure aggregation that guarantees privacy and dropout-resiliency while significantly cutting the overhead for resiliency against dropped users. Utilizing a “one-shot aggregate-mask reconstruction of the active users via mask encoding/decoding” technique, the approach can also be applied to secure aggregation in an asynchronous FL setting. In experiments using diverse training models and datasets, LightSecAgg significantly minimizes total training time.
Benefits
- Privacy and dropout-resiliency parallels state-of-the-art
- Decreases overhead for resiliency
- Allows for scalable implementation
- Increases speed of concurrent receiving and sending of chunked masks
Market Application
Federated learning (FL) allows for distributed learning over a large number of users while maintaining privacy, making it a promising approach for sensitive data applications such as healthcare and finance. FL employs secure model aggregation, which must be resilient to user dropouts. State-of-the-art protocols rely on secret sharing of random-seeds for mask generations to reconstruct and cancel dropped user models. However, this approach's complexity increases as the number of dropouts grows, making a scalable solution a key market need.
Publications
So, Jinhyun, et al. "LightSecAgg: a lightweight and versatile design for secure aggregation in federated learning." Proceedings of Machine Learning and Systems 4 (2022): 694-720.
Stage of Development
- Tested with diverse datasets and training models
- Optimization in progress
- Available for licensing