PDCFL: Prototype‐Driven Decoupled Clustered Federated Learning for Non‐IID Data

Prototype-based federated learning for one-time soft clustering

Experiments under representative Non-IID settings on benchmark datasets show that PFLS-One achieves improved accuracy and faster convergence compared with representative baseline methods, and the convergence analysis under a non-convex objective provides theoretical support for the proposed method.

FedLEAF: Bi-directional Prototype Adaptation for Heterogeneous Federated Learning

Prototype-based knowledge sharing effectively mitigates data and model heterogeneity in federated learning (FL) by exchanging class-level semantic information. However, existing methods typically assume all local prototypes are equally reliable. Consequently, low-quality prototypes from heterogeneous models or dynamic clients can contaminate the global aggregation, leading to a vicious cycle of noise accumulation and performance degradation. To address this, we propose FedLEAF, a Federated Learning framework with server-side proactive Evaluation and clientside Adaptive Fusion. Specifically, the server employs an Adaptive Learning Prototypes (ALP) network to dynamically evaluate prototype reliability and generate learnable aggregation weights, ensuring that highquality prototypes exert a primary influence on the global model. Meanwhile, the client utilizes a Historical Consistency Fusion (HCF) strategy to selectively absorb global knowledge by assessing its consistency with locally maintained historical prototypes. Extensive experiments on standard datasets demonstrate that FedLEAF achieves effective improvements in model accuracy and robustness compared to existing methods.

Metadata Clustering-Driven Federated Learning for Multi-Domain Virtual Network Function Scaling

In multi-domain networking, virtual network function (VNF) scaling using machine learning requires an accurate prediction model while addressing privacy constraints and non-identical and independently distributed (non-IID) data across domains. Current models have used conventional federated learning (FL) methods, such as federated averaging (FedAvg), yet they suffer from degraded performance due to heterogeneous traffic patterns in multi-domain networks. However, existing studies have not addressed the impact of non-IID characteristics on FL-based VNF scaling or developed an effective solution to mitigate it. This paper proposes a metadata-clustering-driven FL method that clusters domains with different traffic patterns and trains cluster-specific models. We extract statistical, spectral, and temporal features to represent traffic disturbance. We apply principal component analysis (PCA) followed by K-means clustering to group time series. We apply FedAvg within clusters to train cluster-specific prediction models. To evaluate the performance of the proposed method, we set up a testbench to synchronize three non-IID patterns. The numerical results demonstrate that the proposed clustered FL method consistently achieves a lower mean squared error (MSE) than the FedAvg baseline across all four evaluated non-IID settings. The proposed method yields an MSE of 0.7056 (a 23.1% reduction from FedAvg’s 0.9176) under label skew, 0.3615 (a 4.6% reduction from 0.3790) under label and feature skew, 0.6958 (a 28.1% reduction from 0.9682) under label and quantity skew, and 0.3748 (a 0.7% reduction from 0.3774) under the combined skew setting. These consistent reductions in MSE demonstrate that the proposed method effectively mitigates the performance degradation typically caused by non-IID effects.

Personalized Federated Learning with Low-Rank Pruning-Based Adaptation for Non-IID Data

Personalized Federated Learning (pFL) has emerged as a promising paradigm, while existing approaches face 3 limitations: granularity mismatch, resource waste, and conflict aggregation. This paper presents a Personalized Federated Learning with Low-rank Pruning-based Adaptation (pFedLoPA) framework. Instead of balancing global and local trade-offs, pFedLoPA decouples the model into client-specific cores and globally shared complements. It integrates low-rank adaptation to constrain optimization to a compact subspace, gradient-based pruning to identify personalized parameters, and complementary aggregation to exchange only relevant updates. This enables clients to retain critical knowledge locally while efficiently integrating global knowledge. Extensive experiments on CIFAR-10/100 with multiple network architectures demonstrate that pFedLoPA outperforms state-of-the-art methods in test accuracy (up to 94.31% on CIFAR-10) while reducing communication costs by over 70%.

SeqFedRPC: Sequential Federated Learning With Regularized Parameter Clustering

This work proposes SeqFedRPC, a novel model decoupling based SFL framework with regularized parameter clustering, and employs a clustering‐based scheme to adaptively decouple the model parameters into shared and personalized subsets, thereby addressing the challenge of non‐IID data.

pFedDS: parameter domain sensitivity based personalized federated learning