Federated Mamba Learning for Privacy-Preserving Collaborative Medical Image Analysis

Abstract

Federated learning has emerged as a promising route to collaborative medical image analysis without raw data exchange, but prevailing architectures often rely on convolutional or attention-based encoders that encounter limitations in modeling long-range dependencies under strict communication and privacy constraints. This paper presents a system-level examination of federated Mamba learning, an approach that integrates selective state space models into distributed medical imaging pipelines. The discussion addresses architectural choices, synchronization trade-offs, privacy accounting, heterogeneous client coordination, governance, clinical deployment, sustainability, robustness, fairness, and policy. Rather than proposing a single algorithm, the paper develops a conceptual and infrastructural framework in which federated Mamba models operate across institutions. The analysis shows that selective state space sequence modeling can reduce some computational bottlenecks of attention while introducing new challenges in gradient compression, calibration, domain shift, interpretability, and auditability. The paper argues that privacy-preserving collaborative analysis depends as much on institutional governance, auditability, and life-cycle carbon accounting as on model architecture. A future-facing agenda is presented for regulated medical data consortia, emphasizing differential privacy, secure aggregation, model cards, and energy-aware training. This synthesis is intended to guide researchers and system designers developing next-generation medical imaging federations.