FedLEAF: Bi-directional Prototype Adaptation for Heterogeneous Federated Learning

Similarity-Aware Personalized Federated Learning in Heterogeneous Environments

Federated Learning (FL) allows decentralized clients to train models collaboratively while preserving data privacy. However, distribution mismatch across clients often leads to poor global generalization and degraded local client-level performance. In such scenarios, some of the clients with their local models trained solely on local data may perform better than the globally learnt model, thus nullifying the benefits of collaborative federated learning. To address this, we propose SAPE-FL (Similarity-Aware Personalized Federated Learning), a novel personalization framework that anchors each client's model to both the global model and a similarity-weighted peer averaged model. By incorporating dynamic, client-specific regularization based on both model similarity and output similarity, SAPE-FL adaptively balances global knowledge transfer and peer collaboration while filtering out dissimilar clients. This dual anchoring mitigates negative transfer and enhances robustness in heterogeneous settings. We theoretically analyze our algorithm establishing its convergence guarantees and empirically show that SAPE-FL outperforms state-of-the-art methods under high statistical heterogeneity and low client data regimes.

FedTopo: Relation-Level Topology Sharing for Model-Heterogeneous Federated Learning

FedTopo is proposed, a relation-level framework that encodes global knowledge as class relation topology, capturing how classes relate within each client rather than where they lie in feature space.

A Meta-Learning-Based Aggregation Strategy for Heterogeneous Federated Learning Scenarios

Federated learning relies on aggregation schemes that assume all participants train models with identical architectures and a common parameter initialization. While this enables parameter-averaging strategies such as Federated Averaging, it also imposes a strong inductive bias by constraining local models to evolve from nearly identical starting points, potentially reducing model diversity and limiting exploration of the hypothesis space. Recent studies have explored shared-initialization-free and heterogeneous federated learning as largely independent research directions. In this context, we propose a shared-initialization-free, architecture-agnostic aggregation strategy based on meta-learning, where local models generate predictions over a reference dataset that are integrated to train a global meta-model. Among the different forms of heterogeneity in federated learning, this work focuses on statistical and model heterogeneity. The proposed framework is evaluated in both one-shot and multi-round federated settings against parameter-averaging and knowledge-distillation approaches while preserving the independent evolution of local models. Experiments across diverse federated settings, including homogeneous and heterogeneous models, varying numbers of participants, different levels of data imbalance on representative benchmark dataset demonstrate that the proposed strategy in a one-shot scenario achieves competitive performance under limited-data conditions, while iterative refinement of the prediction consensus in the multi-round setting yields consistent improvements over traditional aggregation strategies. These findings demonstrate that prediction-space meta-learning constitutes a practical alternative for federated aggregation without requiring shared parameter initialization. Future work will investigate more advanced consensus mechanisms, adaptive historical consensus strategies, and the evaluation of the proposed framework under larger-scale federated learning environments.

FedUP: Uncertainty-Aware Personalized Federated Learning via Probabilistic Prototypes

Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning

Local Inference Guided Aggregation for Heterogeneous Training Environments to Yield Enhancement Through Agreement and Regularization (LIGHTYEAR), a federated learning framework that performs update selection in function space using an NTK-based agreement score to characterize predictive behavior and determine a personalized aggregation set for each client.

FedJigsaw: Multi-Agent Collaborative Model Reassembly for Decentralized Heterogeneous Federated Learning

FedJigsaw is proposed, a novel framework that reshapes model personalization as a dynamic and decentralized model assembly problem that outperforms state-of-the-art MHFL baselines by up to 13.8% in relative accuracy while significantly shrinking cross-client performance variance, but also slashes decision-making latency and peak memory footprint compared to existing policy-driven methods.