Secure Federated Foundation Models with Differentially Private Parameter Exchange

Abstract

The rapid deployment of large-scale foundation models across industry, healthcare, finance, and critical infrastructure has accelerated the need for privacy-preserving, distributed training paradigms. Traditional centralized training pipelines require aggregating sensitive data into a single location, exposing individuals and organizations to significant privacy, security, and compliance risks. Federated Learning (FL) has emerged as a robust alternative, enabling collaborative model training without sharing raw data. However, when extended to foundation models—large-parameter architectures such as LLMs, multimodal transformers, and domain-specific large encoders—FL faces substantial challenges in communication scalability, model leakage, adversarial inference, and system-level security. This research addresses these critical issues by introducing a secure federated foundation model framework that incorporates differentially private parameter exchange, ensuring mathematically provable privacy guarantees while preserving model utility.

 The proposed framework integrates advanced Differential Privacy (DP) mechanisms, such as Gaussian noise calibration, privacy budget accounting, and adaptive clipping, directly into the parameter-exchange pipeline executed at each participating client. Instead of naïvely sharing gradients or parameter deltas, the system enforces per-round privacy bounds using dynamic ε-δ budget allocation, optimizing noise injection according to model sensitivity and gradient sparsity. To address the unique computational and communication burdens associated with foundation models, we propose a hybrid compression–encryption protocol involving secure aggregation, quantization-aware DP clipping, and low-rank parameter exchange. This design reduces bandwidth consumption while enhancing end-to-end security against inference attacks, model reconstruction, and poisoning.

 The proposed system advances the current state of privacy-preserving federated foundation models by offering a unified architecture with verifiable privacy, scalable deployment, and resilience to adversarial behavior. It bridges a critical gap between theoretical DP formulations and their practical implementation in high-capacity models. Beyond technical contributions, the framework supports emerging regulatory and ethical requirements related to AI safety, digital privacy, and responsible data-sharing ecosystems. By enabling secure, collaborative training of foundation models across globally distributed data sources, this research paves the way for future-ready AI systems that uphold privacy by design while delivering state-of-the-art performance.