Discovering Maximum Frequency Consensus: Lightweight Federated Learning for Medical Image Segmentation

Multimodal federated learning (MFL) focuses on integrating distributed multimodal data from different clients to improve feature representation while preserving user privacy and has gained popularity in medical image analysis. Existing research idealizes communication efficiency and generalization ability as independent optimization objectives, leading to the failure in the trade-off between model generalization and client resource constraints. To address this challenge, we propose a lightweight Federated Learning with Mid-Frequency consensus-Driven (FedMFD) method, aiming to efficiently generalize multimodal medical image segmentation tasks while reducing communication costs. In the aspect of communication efficiency, client-side images are converted via Discrete Cosine Transform (DCT) from spatial domain into frequency domain coefficients. With only the mid-frequency components selected for transmission. Regarding generalization capability, we adopt the Earth Mover's Distance (EMD) to quantify the maximum similarity of mid-frequency features between clients, generating a global frequency consensus with the optimal transport plan. Guided by global frequency consensus, client-side structural and detailed representations are reconstructed to improve segmentation generalization in the presence of modality shifts and background noise. Extensive experiments across Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) modality datasets have verified the superiority of FedMFD against its competitors.