Privilege-Preserving Federated Learning for Collaborative Legal AI: An Architecture for Cryptographic Gradient Protection Under Attorney-Client Privilege Constraints

Abstract

Abstract Law firms and corporate legal departments hold large volumes of privileged text that could train superior legal AI models, but attorney-client privilege sharply constrains data sharing across organizational boundaries. Standard federated learning frameworks target statistical privacy rather than the stricter operational requirement that privileged communication content remain inaccessible to non-privileged parties. We present a federated learning architecture designed for multi-firm collaborative model training under explicit privilege constraints. The architecture integrates six components: a privilege classification engine that categorizes documents by privilege type before training; privilege-calibrated differential privacy where noise scales with sensitivity; homomorphic encryption of sanitized gradients with zero-knowledge sanitization proofs; trusted execution environment (TEE)-enclosed aggregation that combines encrypted updates without exposing individual contributions; a privilege boundary graph that models joint defense agreements with dynamic conflict detection and model rollback; and cryptographic audit trails designed for later judicial review. We evaluate the design through formal privacy analysis with composed R\'{e}nyi differential privacy budget bounds, a worked four-entity deployment scenario with conflict detection, and comparative security analysis against baseline federated configurations.