Federated learning (FL) enables multiple clients to learn collaboratively in a distributed way, allowing for privacy protection. However, the real-world nonindependent and identically distributed (non-IID) data will lead to client drift, which degrades the performance of FL. Interestingly, we find that the logit difference between the local and global models increases as the model is continuously updated, which is the primary factor behind performance degradation. This is mainly due to catastrophic forgetting caused by non-IID data between clients. To alleviate this problem, we propose a new algorithm, named FedCSD, a class-prototype similarity distillation in a federated framework to align the logits of local and global models. FedCSD does not simply transfer global knowledge to local clients, as an insufficiently trained global model cannot provide reliable knowledge, i.e., class similarity information, and its wrong soft labels will mislead the optimization of local models. Concretely, FedCSD leverages the similarity between local logits and the global prototype to refine the global logits, thereby enhancing its class similarity information. Furthermore, FedCSD adopts an adaptive mask to filter out the terrible soft labels of the global models, thereby preventing them from misleading local optimization. Extensive experiments demonstrate the superiority of our method over the state-of-the-art FL approaches in various non-IID settings. Code is publicly available at https://github.com/IAMJackYan/FedCSD.

Federated Learning (FL) facilitates collaborative learning among multiple clients in a distributed manner and ensures the security of privacy. However, its performance inevitably degrades with non-Independent and Identically Distributed (non-IID) data. In this paper, we focus on the feature distribution skewed FL scenario, a common non-IID situation in real-world applications where data from different clients exhibit varying underlying distributions. This variation leads to feature shift, which is a key issue of this scenario. While previous works have made notable progress, few pay attention to the data itself, i.e., the root of this issue. The primary goal of this paper is to mitigate feature shift from the perspective of data. To this end, we propose a simple yet remarkably effective input-level data augmentation method, namely FedRDN, which randomly injects the statistical information of the local distribution from the entire federation into the client’s data. This is beneficial to improve the generalization of local feature representations, thereby mitigating feature shift. Moreover, our FedRDN is a plug-and-play component, which can be seamlessly integrated into the data augmentation flow with only a few lines of code. Extensive experiments on several datasets show that the performance of various representative FL methods can be further improved by integrating our FedRDN, demonstrating its effectiveness, strong compatibility and generalizability. Code is available at https://github.com/IAMJackYan/FedRDN.

Non-Independent and Identically Distributed (Non-IID) training data significantly challenge federated learning (FL), impairing the performance of the global model in distributed frameworks. Inspired by the superior performance and generalizability of language-driven representation learning in centralized settings, we explore its potential to enhance FL for handling non-IID data. In specific, this paper introduces FedGLCL, a novel language-driven FL framework for image-text learning that uniquely integrates global language and local image features through contrastive learning, offering a new approach to tackle non-IID data in FL. FedGLCL redefines FL by avoiding separate local training models for each client. Instead, it uses contrastive learning to harmonize local image features with global textual data, enabling uniform feature learning across different local models. The utilization of a pre-trained text encoder in FedGLCL serves a dual purpose: it not only reduces the variance in local feature representations within FL by providing a stable and rich language context but also aids in mitigating overfitting, particularly to majority classes, by leveraging broad linguistic knowledge. Extensive experiments show that FedGLCL significantly outperforms state-of-the-art FL algorithms across different non-IID scenarios.

Low-Rank Adaptation (LoRA) presents an effective solution for federated fine-tuning of Large Language Models (LLMs), as it substantially reduces communication overhead. However, a straightforward combination of FedAvg and LoRA results in suboptimal performance, especially under data heterogeneity. We noted this stems from both intrinsic (i.e., constrained parameter space) and extrinsic (i.e., client drift) limitations, which hinder it effectively learn global knowledge. In this work, we proposed a novel Federated Residual Low-Rank Adaption method, namely FRLoRA, to tackle above two limitations. It directly sums the weight of the global model parameters with a residual low-rank matrix product (\ie, weight change) during the global update step, and synchronizes this update for all local models. By this, FRLoRA performs global updates in a higher-rank parameter space, enabling a better representation of complex knowledge structure. Furthermore, FRLoRA reinitializes the local low-rank matrices with the principal singular values and vectors of the pre-trained weights in each round, to calibrate their inconsistent convergence, thereby mitigating client drift. Our extensive experiments demonstrate that FRLoRA consistently outperforms various state-of-the-art FL methods across nine different benchmarks in natural language understanding and generation under different FL scenarios.

While multi-modal learning has been widely used for MRI reconstruction, it relies on paired multi-modal data, which is difficult to acquire in real clinical scenarios. Especially in the federated setting, there is a common issue that several medical institutions suffer from missing modalities or even only have single-modal data. Therefore, it is infeasible to deploy a standard federated learning framework in such conditions. In this paper, we propose a novel communication-efficient federated learning framework (namely Fed-PMG) to address the missing modality challenge in federated multi-modal MRI reconstruction. Specifically, we utilize a pseudo modality generation mechanism to recover the missing modality for each single-modal client by sharing the distribution information of the amplitude spectrum in frequency space. However, the step of sharing the original amplitude spectrum leads to heavy communication costs. To reduce the communication cost, we introduce a clustering scheme to project the set of amplitude spectrum into a finite number of cluster centroids and share them among the clients. With such an elaborate design, our approach can effectively complete the missing modality within an acceptable communication cost. Extensive experimental results demonstrate that our proposed method can outperform state-of-the-art methods and reach a performance similar to the ideal scenario (i.e., all clients have the full set of modalities).

Federated learning enables multiple hospitals to cooperatively learn a shared model without privacy disclosure. Existing methods often take a common assumption that the data from different hospitals have the same modalities. However, such a setting is difficult to fully satisfy in practical applications, since the imaging guidelines may be different between hospitals, which makes the number of individuals with the same set of modalities limited. To this end, we formulate this practical-yet-challenging cross-modal vertical federated learning task, in which data from multiple hospitals have different modalities with a small amount of multi-modality data collected from the same individuals. To tackle such a situation, we develop a novel framework, namely Federated Consistent Regularization constrained Feature Disentanglement (Fed-CRFD), for boosting MRI reconstruction by effectively exploring the overlapping samples (i.e., same patients with different modalities at different hospitals) and solving the domain shift problem caused by different modalities. Particularly, our Fed-CRFD involves an intra-client feature disentangle scheme to decouple data into modality-invariant and modality-specific features, where the modality-invariant features are leveraged to mitigate the domain shift problem. In addition, a cross-client latent representation consistency constraint is proposed specifically for the overlapping samples to further align the modality-invariant features extracted from different modalities. Hence, our method can fully exploit the multi-source data from hospitals while alleviating the domain shift problem. Extensive experiments on two typical MRI datasets demonstrate that our network clearly outperforms state-of-the-art MRI reconstruction methods.

Super-resolving the magnetic resonance (MR) image of a target contrast under the guidance of the corresponding auxiliary contrast, which provides additional anatomical information, is a new and effective solution for fast MR imaging. However, current multi-contrast super-resolution (SR) methods tend to concatenate different contrasts directly, ignoring their relationships in different clues, e.g., in the high-and low-intensity regions. In this study, we propose a separable attention network (comprising high-intensity priority (HP) attention and low-intensity separation (LS) attention), named SANet. Our SANet could explore the areas of high-and low-intensity regions in the "forward" and "reverse" directions with the help of the auxiliary contrast while learning clearer anatomical structure and edge information for the SR of a target-contrast MR image. SANet provides three appealing benefits: First, it is the first model to explore a separable attention mechanism that uses the auxiliary contrast to predict the high-and low-intensity regions, diverting more attention to refining any uncertain details between these regions and correcting the fine areas in the reconstructed results. Second, a multistage integration module is proposed to learn the response of multi-contrast fusion at multiple stages, get the dependency between the fused representations, and boost their representation ability. Third, extensive experiments with various state-of-the-art multi-contrast SR methods on fastMRI and clinical in vivo datasets demonstrate the superiority of our model. The code is released at https://github.com/chunmeifeng/SANet.

Federated learning (FL) can be used to improve data privacy and efficiency in magnetic resonance (MR) image reconstruction by enabling multiple institutions to collaborate without needing to aggregate local data. However, the domain shift caused by different MR imaging protocols can substantially degrade the performance of FL models. Recent FL techniques tend to solve this by enhancing the generalization of the global model, but they ignore the domain-specific features, which may contain important information about the device properties and be useful for local reconstruction. In this paper, we propose a specificity-preserving FL algorithm for MR image reconstruction (FedMRI). The core idea is to divide the MR reconstruction model into two parts: a globally shared encoder to obtain a generalized representation at the global level, and a client-specific decoder to preserve the domain-specific properties of each client, which is important for collaborative reconstruction when the clients have unique distribution. Such scheme is then executed in the frequency space and the image space respectively, allowing exploration of generalized representation and client-specific properties simultaneously in different spaces. Moreover, to further boost the convergence of the globally shared encoder when a domain shift is present, a weighted contrastive regularization is introduced to directly correct any deviation between the client and server during optimization. Extensive experiments demonstrate that our FedMRI's reconstructed results are the closest to the ground-truth for multi-institutional data, and that it outperforms state-of-the-art FL methods.

Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution for fast MR imaging, providing superior performance in restoring the target modality from its undersampled counterpart with guidance from an auxiliary modality. However, existing works simply combine the auxiliary modality as prior information, lacking in-depth investigations on the potential mechanisms for fusing different modalities. Further, they usually rely on the convolutional neural networks (CNNs), which is limited by the intrinsic locality in capturing the long-distance dependency. To this end, we propose a multi-modal transformer (MTrans), which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging. To capture deep multi-modal information, our MTrans utilizes an improved multi-head attention mechanism, named cross attention module, which absorbs features from the auxiliary modality that contribute to the target modality. Our framework provides three appealing benefits: (i) Our MTrans use an improved transformers for multi-modal MR imaging, affording more global information compared with existing CNN-based methods. (ii) A new cross attention module is proposed to exploit the useful information in each modality at different scales. The small patch in the target modality aims to keep more fine details, the large patch in the auxiliary modality aims to obtain high-level context features from the larger region and supplement the target modality effectively. (iii) We evaluate MTrans with various accelerated multi-modal MR imaging tasks, e.g., MR image reconstruction and super-resolution, where MTrans outperforms state-of-the-art methods on fastMRI and real-world clinical datasets.