CA-AFP: Cluster-Aware Adaptive Federated Pruning

• URL: http://arxiv.org/abs/2603.01739v1
• Date: Mon, 02 Mar 2026 11:04:25 GMT
• Title: CA-AFP: Cluster-Aware Adaptive Federated Pruning
• Authors: Om Govind Jha, Harsh Shukla, Haroon R. Lone,
• Abstract summary: Federated Learning (FL) faces major challenges in real-world deployments due to statistical heterogeneity across clients.<n>We propose CA-AFP, a unified framework that jointly addresses both challenges by performing cluster-specific model pruning.<n>We evaluate CA-AFP on two widely used human activity recognition benchmarks, UCI HAR and WISDM, under natural user-based federated partitions.
• Score: 1.345821655503426
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Learning (FL) faces major challenges in real-world deployments due to statistical heterogeneity across clients and system heterogeneity arising from resource-constrained devices. While clustering-based approaches mitigate statistical heterogeneity and pruning techniques improve memory and communication efficiency, these strategies are typically studied in isolation. We propose CA-AFP, a unified framework that jointly addresses both challenges by performing cluster-specific model pruning. In CA-AFP, clients are first grouped into clusters, and a separate model for each cluster is adaptively pruned during training. The framework introduces two key innovations: (1) a cluster-aware importance scoring mechanism that combines weight magnitude, intra-cluster coherence, and gradient consistency to identify parameters for pruning, and (2) an iterative pruning schedule that progressively removes parameters while enabling model self-healing through weight regrowth. We evaluate CA-AFP on two widely used human activity recognition benchmarks, UCI HAR and WISDM, under natural user-based federated partitions. Experimental results demonstrate that CA-AFP achieves a favorable balance between predictive accuracy, inter-client fairness, and communication efficiency. Compared to pruning-based baselines, CA-AFP consistently improves accuracy and lower performance disparity across clients with limited fine-tuning, while requiring substantially less communication than dense clustering-based methods. It also shows robustness to different Non-IID levels of data. Finally, ablation studies analyze the impact of clustering, pruning schedules and scoring mechanism offering practical insights into the design of efficient and adaptive FL systems.

Related papers

• Adaptive Dual-Weighting Framework for Federated Learning via Out-of-Distribution Detection [53.45696787935487]
  Federated Learning (FL) enables collaborative model training across large-scale distributed service nodes.<n>In real-world service-oriented deployments, data generated by heterogeneous users, devices, and application scenarios are inherently non-IID.<n>We propose FLood, a novel FL framework inspired by out-of-distribution (OOD) detection.
  arXiv  Detail & Related papers  (2026-02-01T05:54:59Z)
• Towards Federated Clustering: A Client-wise Private Graph Aggregation Framework [57.04850867402913]
  Federated clustering addresses the challenge of extracting patterns from decentralized, unlabeled data.<n>We propose Structural Privacy-Preserving Federated Graph Clustering (SPP-FGC), a novel algorithm that innovatively leverages local structural graphs as the primary medium for privacy-preserving knowledge sharing.<n>Our framework achieves state-of-the-art performance, improving clustering accuracy by up to 10% (NMI) over federated baselines while maintaining provable privacy guarantees.
  arXiv  Detail & Related papers  (2025-11-14T03:05:22Z)
• Interaction-Aware Gaussian Weighting for Clustered Federated Learning [58.92159838586751]
  Federated Learning (FL) emerged as a decentralized paradigm to train models while preserving privacy.<n>We propose a novel clustered FL method, FedGWC (Federated Gaussian Weighting Clustering), which groups clients based on their data distribution.<n>Our experiments on benchmark datasets show that FedGWC outperforms existing FL algorithms in cluster quality and classification accuracy.
  arXiv  Detail & Related papers  (2025-02-05T16:33:36Z)
• Client-Centric Federated Adaptive Optimization [78.30827455292827]
  Federated Learning (FL) is a distributed learning paradigm where clients collaboratively train a model while keeping their own data private.<n>We propose Federated-Centric Adaptive Optimization, which is a class of novel federated optimization approaches.
  arXiv  Detail & Related papers  (2025-01-17T04:00:50Z)
• Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration [66.43954501171292]
  We introduce Catalyst Acceleration and propose an acceleration Decentralized Federated Learning algorithm called DFedCata. DFedCata consists of two main components: the Moreau envelope function, which addresses parameter inconsistencies, and Nesterov's extrapolation step, which accelerates the aggregation phase. Empirically, we demonstrate the advantages of the proposed algorithm in both convergence speed and generalization performance on CIFAR10/100 with various non-iid data distributions.
  arXiv  Detail & Related papers  (2024-10-09T06:17:16Z)
• An Architecture Built for Federated Learning: Addressing Data Heterogeneity through Adaptive Normalization-Free Feature Recalibration [0.3481075494213406]
  We propose Adaptive Normalization-free Feature Recalibration (ANFR) to combat heterogeneous data in Federated Learning (FL)<n>ANFR combines weight standardization and channel attention to produce learnable scaling factors for feature maps.<n>Experiments show ANFR consistently outperforms established baselines across various aggregation methods.
  arXiv  Detail & Related papers  (2024-10-02T20:16:56Z)
• FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data [21.341280782748278]
  Clustered federated learning (CFL) is proposed to mitigate the performance deterioration stemming from data heterogeneity inFL. We propose an adaptive CFL framework, named FedAC, which efficiently integrates global knowledge into intra-cluster learning. Experiments show that FedAC achieves superior empirical performance, increasing the test accuracy by around 1.82% and 12.67%.
  arXiv  Detail & Related papers  (2024-03-25T06:43:28Z)
• Federated cINN Clustering for Accurate Clustered Federated Learning [33.72494731516968]
  Federated Learning (FL) presents an innovative approach to privacy-preserving distributed machine learning. We propose the Federated cINN Clustering Algorithm (FCCA) to robustly cluster clients into different groups.
  arXiv  Detail & Related papers  (2023-09-04T10:47:52Z)
• Federated Two Stage Decoupling With Adaptive Personalization Layers [5.69361786082969]
  Federated learning has gained significant attention due to its ability to enable distributed learning while maintaining privacy constraints. It inherently experiences significant learning degradation and slow convergence speed. It is natural to employ the concept of clustering homogeneous clients into the same group, allowing only the model weights within each group to be aggregated.
  arXiv  Detail & Related papers  (2023-08-30T07:46:32Z)
• Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction [76.26710990597498]
  We show that the class-imbalance of the grouped data from randomly selected clients can lead to significant performance degradation. Based on our key observation, we design an efficient client sampling mechanism, i.e., Federated Class-balanced Sampling (Fed-CBS) In particular, we propose a measure of class-imbalance and then employ homomorphic encryption to derive this measure in a privacy-preserving way.
  arXiv  Detail & Related papers  (2022-09-30T05:42:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.