Private Federated Learning Model Update Privacy

The patent segments model updates into multiple components (e.g., gradient updates, intermediate parameters) and transmits them separately through multiple rounds of communication. This segmentation prevents adversaries from reconstructing the complete model update from a single transmission, thereby protecting user privacy while maintaining learning utility through cumulative aggregation of segmented updates.

The patent introduces trusted intermediaries (such as aggregators or mediators) that receive, verify, and aggregate model updates from multiple devices before forwarding to the server. These intermediaries act as protective layers that prevent direct exposure of raw model updates to potential adversaries, enabling privacy-pres federated learning without sacrificing collaborative learning effectiveness.

The patent applies partial differential privacy by adding noise only to specific sensitive components of model updates rather than to the entire model. This selective approach provides sufficient privacy protection for critical information while minimizing the computational overhead and communication costs associated with applying privacy mechanisms to all model parameters.

The patent dynamically adjusts privacy parameters (such as noise magnitude, segmentation granularity, or aggregation thresholds) based on the sensitivity of the data and the requirements of the learning task. This adaptive parameter adjustment allows the system to achieve adequate privacy protection with minimal computational and communication overhead, avoiding the excessive costs of fixed strong privacy mechanisms.

The patent extracts and removes the trust requirement from the server by implementing client-side verification mechanisms and cryptographic proofs. Instead of relying on the server to be trusted, the system extracts the verification functionality to the client devices, allowing untrusted servers to participate in federated learning while maintaining privacy security through decentralized verification.

The patent implements self-service privacy protection where each device independently verifies the privacy compliance of model updates using local cryptographic checks and validation rules. This self-service approach eliminates the need for centralized trusted authorities, reducing system complexity and trust requirements while maintaining robust privacy security through distributed verification capabilities.