Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation
arXiv:2604.07125v2 Announce Type: replace-cross Abstract: This article presents DDP-SA, a scalable privacy-preserving federated learning framework that jointly leverages client-side local differential privacy (LDP) and full-threshold additive secret sharing (ASS) for secure aggregation. Unlike existing methods that rely solely on differential privacy or on secure multi-party computation (MPC), DDP-SA integrates both techniques to deliver stronger end-to-end privacy guarantees while remaining computationally practical. The framework introduces a two-stage protection mechanism: clients first per
The increasing concern around data privacy and the proliferation of AI models necessitate robust solutions for secure collaborative learning without compromising sensitive information. Recent advancements in cryptographic techniques and distributed systems also make such frameworks increasingly viable.
This development is crucial for industries handling sensitive data, enabling the utilization of federated learning while adhering to strict privacy regulations and building trust among participants. It accelerates the deployment of AI in regulated sectors.
This framework offers an improved method for scalable and private federated learning, reducing the trade-offs between privacy guarantees and computational practicality. It provides a more secure approach to model training across a distributed network.
- · Healthcare sector
- · Financial institutions
- · AI/ML developers
- · Data privacy solution providers
- · Companies with weak data privacy practices
- · Less secure federated learning frameworks
Wider adoption of federated learning in privacy-sensitive domains will accelerate AI model development and deployment.
Increased trust in AI systems could lead to more data sharing for collaborative research and development while maintaining privacy.
The enhanced privacy infrastructure might encourage new business models that rely on secure, decentralized data processing without direct data exposure.
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