CA-AFP: Cluster-Aware Adaptive Federated Pruning

Om Govind Jha; Harsh Shukla; Haroon R. Lone

cs.LGMar 2, 2026

CA-AFP: Cluster-Aware Adaptive Federated Pruning

Authors: Om Govind Jha, Harsh Shukla, Haroon R. Lone

Organizations: IISER Bhopal Bhopal, India

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.

Figures & tables

Figure 1. The overall workflow of CA-AFP.

Heterogeneity Type	Metric	WISDM	UCI HAR
Quantity Skew	Coef. of Variation ( $CV=\frac{\sigma}{\mu}\times 100\%$ )	30.4%	10.2%
	Sample Count ( $\mu\pm\sigma$ )	$297\pm 90$	$343\pm 35$
Label Skew	Clients with Missing Classes	17 / 36	0 / 30
	Missing Rate	47.2%	0.0%
Feature Skew	Mean Inter-Client CV	6.5%	0.6%

Table 1. Heterogeneity analysis of WISDM and UCI-HAR.

			WISDM			UCI-HAR
Method	Sparsity	FT Epochs	Avg Acc ( $\mu$ )	Fairness ( $\sigma$ )	Comm	Avg Acc ( $\mu$ )	Fairness ( $\sigma$ )	Comm
			( $\uparrow$ )	( $\downarrow$ )	(MB)	( $\uparrow$ )	( $\downarrow$ )	(MB)
Dense Baseline
FedCHAR ( Li et al., 2023b )	0%	3	96.64%	8.39%	483	96.87%	6.70%	347
	0%	25	97.16%	8.32%	483	97.48%	6.19%	347
ClusterFL ( Ouyang et al., 2023 )	0%	3	96.18%	7.02%	474	95.80%	6.87%	340

Table 2. Performance comparison on the WISDM and UCI-HAR datasets with 3 and 25 fine-tuning (FT) epochs.

Scenario	Dataset	$\alpha$	$\beta$	$\gamma$	Avg Acc (%)	Fairness (%)
Standard	UCI HAR	0.25	0.25	0.50	91.19	9.81
	WISDM	0.25	0.25	0.50	91.57	10.51
Drift	UCI HAR	0.25	0.25	0.50	66.54	32.70
	WISDM	0.00	0.00	1.00	57.63	33.12
Noisy Clients	UCI HAR	0.50	0.25	0.25	74.42	19.03
	WISDM	0.25	0.25	0.50	74.36	20.72

Table 3. Best “importance score” configurations.

Figure 2. Effect of “Start Sparsity” and “Rounds” on accuracy in WISDM.

Figure 3. Impact of Local Fine-Tuning on accuracy in WISDM.

Figure 4. Performance across three Non-IID levels.

	Weights			UCI HAR			WISDM
Scenario	$\alpha$	$\beta$	$\gamma$	Acc	Fairness	Score	Acc	Fairness	Score
Drift	1.00	0.00	0.00	0.6860	0.3526	1.9453	0.6129	0.3820	1.6047
Drift	0.00	1.00	0.00	0.6339	0.3148	2.0135	0.5915	0.3456	1.7116
Drift	0.00	0.00	1.00	0.6475	0.3264	1.9840	0.5763	0.3312	1.7399
Drift	0.50	0.25	0.25	0.6939	0.3452	2.0102	0.5774	0.3396	1.7001
Drift	0.25	0.50	0.25	0.6853	0.3546	1.9323	0.5974	0.3671	1.6273

Table 4. Combined ablation study of importance score weights

(\alpha,\beta,\gamma)

on UCI HAR and WISDM datasets. Note: "Extreme Non-IID" is excluded as it was not evaluated on WISDM.

Method	Parameter	Value
Common Settings	Rounds phase wise ( $P_{1},P_{2},P_{3}$ )	0, 0, 50
	Local Epochs ( $E$ )	3
	Batch Size ( $B$ )	32
	Optimizer	Adam ( $lr=1e^{-3}$ )
	Fine-Tuning Rounds ( $P_{4}$ )	3
CA-AFP (Ours)	Target Sparsity	Adaptive (Start 70%)

Table 5. Hyperparameter Configuration for UCI HAR Experiments

Method	Parameter	Value
Common Settings	Rounds phase wise ( $P_{1},P_{2},P_{3}$ )	0, 0, 50
	Local Epochs ( $E$ )	3
	Batch Size ( $B$ )	32
	Optimizer	Adam ( $lr=1e^{-3}$ )
	Fine-Tuning Rounds ( $P_{4}$ )	25
CA-AFP (Ours)	Target Sparsity	Adaptive (Start 70%)

Table 6. Configuration for WISDM Experiments

Ablation Study	$P_{1}$	$P_{2}$	$P_{3}$	$P_{4}$	Start
	(Warmup)	(Adapt)	(Prune)	(Fine-Tune)	Sparsity
Study A: Start Sparsity	0	0	40	3	Varied
(Varying $S_{start}$ )					( $0.0-0.9$ )
Study B: Post-Cluster	0	Varied	40	3	0.5
(Varying $P_{2}$ )		$\{0,5,\dots,20\}$			(50%)
Study C: Initial Warmup	Varied	0	40	3	0.5

Table 7. Phase Configuration for CA-AFP Ablation Studies (WISDM)

Component	Parameter	Value / Description
Scoring Weights	Grid Search	$(1.0,0.0,0.0)$ – Pure Magnitude
( $\alpha,\beta,\gamma$ )	(7 Combinations)	$(0.0,1.0,0.0)$ – Pure Coherence
		$(0.0,0.0,1.0)$ – Pure Consistency
		$(0.50,0.25,0.25)$ – Magnitude Dominant
		$(0.25,0.50,0.25)$ – Coherence Dominant
		$(0.25,0.25,0.50)$ – Consistency Dominant

Table 8. Configuration for Importance Score Ablation Study

Component	Parameter	Value
Dataset	Scenario	WISDM (Standard)
	Partitioning	Natural User Split
Phase 1, 2 and 3	Rounds ( $P_{1},P_{2},P_{3}$ )	(0, 0, 50)
	Sparsity Target	70%
	Clients per Round	10
Phase 4: Personalization	Rounds ( $P_{4}$ )	{1, 5, 10, 15, 20, 25}

Table 9. Configuration for Fine-Tuning Ablation

Non-IID Scenarios
Parameter	Configuration
Dataset	WISDM (Activity Recognition)
Total Clients	36 (Heterogeneous)
Global Rounds	50
Target Sparsity	70% ( $0.7$ )
Runs per Scenario	5 Independent Seeds

Table 10. Configuration for Non-IID Experiment

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Federated learning on heterogeneous edge devices requires personalized compression while preserving aggregation compatibility and stable convergence. We present Curvature-Aware Heterogeneous Federated Pruning (CA-HFP), a practical framework that enables each client perform structured, device-specific pruning guided by a curvature-informed significance score, and subsequently maps its compact submodel back into a common global parameter space via a lightweight reconstruction. We derive a convergence bound for federated optimization with multiple local SGD steps that explicitly accounts for local computation, data heterogeneity, and pruning-induced perturbations; from which a principled loss-based pruning criterion is derived. Extensive experiments on FMNIST, CIFAR-10, and CIFAR-100 using VGG and ResNet architectures under varying degrees of data heterogeneity demonstrate that CA-HFP preserves model accuracy while significantly reducing per-client computation and communication costs, outperforming standard federated training and existing pruning-based baselines.

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