FedMCP++: Integrating Modular Expert Heads with Prototype-Guided Contrastive Distillation for Wireless Personalized Federated Learning

FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning

Experimental results show that FedOPAL not only significantly outperforms the original analytical methods on several benchmarks, but also achieves accuracy comparable to state-of-the-art iterative methods while maintaining zero server-side training costs, providing a new engineering paradigm for efficient collaboration of large models on the edge.

One-Shot Federated Learning With Lightweight Intermediate Models in Wireless Sensor Network Setting

Experimental results on three datasets demonstrate that FedLIM achieves superior global model accuracy compared to existing one-shot FL methods, particularly in highly heterogeneous environments.

CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning

One-shot federated learning (OSFL) has emerged as a promising collaborative model learning framework with only a single round of communication, offering significant advantages in communication efficiency and privacy preservation. However, OSFL often faces inherent limitations under severe domain heterogeneity across clients due to the lack of iterative knowledge exchange. Most existing OSFL methods require an auxiliary public dataset for knowledge distillation or leverage statistical information for parameter-level aggregation, overlooking feature shift caused by domain heterogeneity. To address these challenges, we propose CRIP, a personalized OSFL framework that operates in the representation space via channel-level feature alignment. To achieve this, each client uploads its feature extractor to the server, which broadcasts all extractors back to every client. Since not all source clients share compatible feature distributions with the target client, indiscriminate fusion of cross-client features would introduce domain-specific noise. Therefore, CRIP effectively measures the channel-wise representational similarity between the target client and each source client on a small local mini-batch, and selectively fuses only the most compatible features. Extensive experiments on domain-heterogeneous benchmarks such as DomainNet, PACS, and Office-Home demonstrate that CRIP consistently outperforms local models and state-of-the-art baselines, validating the effectiveness of representation-space personalization under extreme domain heterogeneity.

Fine-grained prototype category alignment for heterogeneous data in federated learning

BiFCL : A Bi‐Level Federated Contrastive Learning Framework With Auxiliary Model‐Guided Alignment

Federated learning (FL) enables collaborative model training over distributed clients without sharing raw data, but its performance can degrade severely under non‐independent and non ‐ identically distributed (non‐IID) client data. To address this challenge, we propose BiFCL , a Bi‐Level Federated Contrastive Learning framework that improves global representation alignment through a lightweight server‐side auxiliary model trained on a small public dataset. BiFCL enforces bidirectional semantic consistency: on the client side, each local model is regularized by contrastively aligning its representations to the auxiliary model; on the server side, the auxiliary model is further updated via contrastive alignment with the evolving global model, preventing stale guidance and forming a closed‐loop regularization mechanism. We provide convergence guarantees for BiFCL under standard smooth non‐convex assumptions. Extensive experiments on five benchmarks (MNIST, EMNIST, SVHN, CIFAR‐10 and CIFAR‐100) demonstrate that BiFCL consistently outperforms strong FL baselines in both final accuracy and convergence speed. Overall, BiFCL provides an effective representation‐alignment strategy for heterogeneous federated learning when a small, diverse, and semantically relevant public, proxy or privacy‐preserving synthetic auxiliary source is available. We also discuss practical auxiliary‐data construction strategies, normalized communication cost and limitations under severe auxiliary‐domain mismatch and larger‐client deployments.

FedAli-PHAR: personalized federated learning with optimal transport-based prototype alignment for robust human activity recognition in pervasive computing