Federated Learning with Enhanced Privacy via Model Splitting and Random Client Participation
arXiv:2509.25906v2 Announce Type: replace Abstract: Federated Learning (FL) often adopts differential privacy (DP) to protect client data, but the added noise required for privacy guarantees can substantially degrade model accuracy. To resolve this challenge, we propose model-splitting privacy-amplified federated learning (MS-PAFL), a novel framework that combines structural model splitting with statistical privacy amplification. In this framework, each client's model is partitioned into a private submodel, retained locally, and a public submodel, shared for global aggregation. The calibrated
The increasing adoption of Federated Learning in sensitive data environments necessitates advanced privacy-preserving techniques to overcome accuracy degradation challenges.
This development offers a practical pathway to deploy secure and effective AI systems in healthcare, finance, and other privacy-critical sectors, bypassing previous trade-offs between privacy and model performance.
The ability to achieve enhanced privacy in federated learning without substantial accuracy loss shifts the landscape for secure distributed AI development and deployment.
- · Healthcare sector
- · Financial institutions
- · Privacy-focused AI developers
- · Edge computing providers
- · AI frameworks with poor privacy controls
- · Organizations reliant on centralized data models
Wider adoption of Federated Learning in regulated industries due to improved privacy guarantees and model utility.
Increased trust in AI applications that process sensitive user data, potentially accelerating AI integration into daily life.
New regulatory frameworks may emerge to standardize and certify such privacy-enhanced federated learning approaches, shaping future data governance.
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Read at arXiv cs.LG