Rethinking Cross-Task Knowledge Sharing: Medical Federated Multi-Task Learning with the Input Space Knowledge Decoupling

Federated multi-task learning (FMTL) seeks to share information across institutions and heterogeneous tasks while preserving client-specific personalization. Existing methods use local task models, their parameters, representations, or outputs, as carriers for federated knowledge sharing. Because potentially transferable and task-specific information are entangled within these carriers, aggregation cannot determine what should be shared and what should remain local: insufficient sharing may miss beneficial knowledge, whereas excessive sharing can cause negative transfer. To address this conflict, we propose IPL-FL, an FMTL framework that keeps personalization-specific knowledge local while separating cross-client knowledge sharing from local personalization, thereby avoiding the knowledge-selection conflict caused by their coupling. IPL-FL keeps task-specific knowledge in frozen local task models and instead communicates input-adaptation generators, preventing local predictors from being directly modified by aggregation. IPL-FL comprises client-side task-guided perturbation learning (TGPL) and server-side relation-aware personalized aggregation (RAPA). TGPL uses local task supervision to optimize task-beneficial input perturbations and transfers the resulting adaptations to shareable generators. RAPA decomposes generator updates into candidate shared information and client-specific residuals, and selectively aggregates compatible and complementary information using update-direction consistency and conditional information gain. Across eight clients spanning three medical tasks with heterogeneous architectures, IPL-FL improves every client over local training, achieves a 5.65% gain, exceeds the strongest applicable FMTL baseline by 5.27%, and offers a 13% efficiency advantage.