PACE: Propagation-Aware Collaborative Correction for One-Shot Personalized Federated Graph Learning

Ruizhe Huang; Chengran Li; Xiaochuan Shi

cs.LGSep 4, 2026

PACE: Propagation-Aware Collaborative Correction for One-Shot Personalized Federated Graph Learning

Authors: Ruizhe Huang, Chengran Li, Xiaochuan Shi

Organizations: School of Cyber Science and Engineering, Wuhan University

Abstract

Client heterogeneity creates both an opportunity and a risk in personalized federated graph learning. Knowledge held by other subgraphs may complement a receiver's Local model, but an incompatible transfer can override reliable predictions. One-shot communication sharpens this tension because an unsuitable server return cannot be corrected later. We introduce PACE, which treats collaborative knowledge as a compact correction to a complete Local predictor rather than as its replacement. Each client uploads a rank-r update carrier and a diagonal sketch of propagated message moments. The server uses them to construct a propagation-aware, receiver-anchored correction, while the receiver retains its full Local model. Convex negative-log-likelihood calibration (CNLL) then selects one coefficient between Local and External logits using validation nodes; model parameters remain fixed and no feedback is sent. At Rank-6, personalized returns occupy 9.6-17.6% of dense tensor bytes across the six evaluated datasets. The correction receives nonzero weight and improves both Accuracy and weighted-F1 over Local on five datasets; on ogbn-arxiv, CNLL assigns zero predictive weight to the correction and preserves Local predictions exactly. Applying the same CNLL rule to matched baselines on three citation datasets does not account for these gains. The central result is therefore that a small transported correction can augment a complete Local model when receiver evidence supports it while leaving the Local prediction unchanged otherwise.

Figures & tables

Figure 1: Two coupled challenges in one-shot personalized FGL. Compact communication must preserve complementary evidence across clients, while the utility of shared knowledge depends on the receiver and can become negative under mismatch.

Figure 2: PACE pipeline. Each client trains locally and uploads a propagation-aware Rank-6 carrier once. The server forms a self-inclusive RegMean consensus and returns a receiver-anchored Rank-6 correction. Each receiver then calibrates a single convex logit coefficient with validation NLL, without parameter updates, test-label selection, feedback, or additional communication.

Methods	Cora		CiteSeer		PubMed
	Accuracy	W-F1	Accuracy	W-F1	Accuracy	W-F1
Local	79.16 $\pm$ 0.25	79.08 $\pm$ 0.27	65.41 $\pm$ 0.72	64.70 $\pm$ 0.73	84.25 $\pm$ 0.05	84.19 $\pm$ 0.05
FedAvg	33.18 $\pm$ 0.94	20.28 $\pm$ 1.74	72.79 $\pm$ 0.32	69.81 $\pm$ 0.46	78.21 $\pm$ 1.07	76.49 $\pm$ 1.49
FedProx	39.61 $\pm$ 2.45	30.88 $\pm$ 3.61	72.82 $\pm$ 0.32	70.12 $\pm$ 0.34	72.32 $\pm$ 2.21	68.28 $\pm$ 2.37
FedNova	29.84 $\pm$ 0.47	14.15 $\pm$ 0.62	38.33 $\pm$ 2.04	33.11 $\pm$ 3.43	38.33 $\pm$ 3.44	29.41 $\pm$ 5.92
FedRCL	23.57 $\pm$ 8.89	15.44 $\pm$ 6.53	26.31 $\pm$ 3.14	18.02 $\pm$ 3.37	35.70 $\pm$ 8.32	19.27 $\pm$ 6.76

Table 1: Results with 10 clients under Louvain partitioning. Values are five-seed mean

\pm

sample standard deviation in percent. Bold and underline denote the best and second-best distinct displayed means, respectively.

Variant	Cora	CiteSeer	PubMed
Local	79.16	65.41	84.25
Transport ( $\alpha=1$ )	63.46	73.82	83.66
Fixed $\alpha=0.5$	80.09	69.96	84.97
Mean + CNLL	79.19	71.77	84.37
Raw-Activation RegMean + CNLL	80.32	73.38	84.79
Direct Return + CNLL	79.91	73.58	84.80

Table 2: Component ablation at

C=10

over five seeds on the three citation benchmarks. Values are mean Accuracy (%). Bold and underline denote the best and second-best distinct displayed means, respectively.

Figure 3: Rank sensitivity and communication at

C=10

. Dashed lines mark Local; the dotted line marks frozen Rank-6; Dense is the uncompressed control.

Dataset	Mean $\alpha$	$\alpha=0$	Upload	Downlink
Cora	0.655	0.0%	12.1%	10.3%
CiteSeer	0.979	0.0%	11.4%	9.7%
PubMed	0.278	0.0%	13.6%	11.4%
CS	0.512	0.0%	11.2%	9.6%
Computers	0.134	22.0%	13.1%	11.1%
ogbn-arxiv	0.000	100.0%	21.6%	17.6%

Table 3: PACE communication and CNLL utilization at

C=10

. Ratios use complete serialized Rank-6 PACE upload/download bytes relative to dense tensor bytes.

Accuracy
Dataset	Matched method	Score	PACE	Gap
Cora	FedAux-1R+CNLL	79.69	80.34	+0.65
CiteSeer	FedAvg+CNLL	73.11	73.82	+0.71
PubMed	FedTAD+CNLL	84.44	84.79	+0.35
Weighted-F1
Dataset	Matched method	Score	PACE	Gap

Table 4: Matched-CNLL fairness summary at

C=10

over five seeds. For each metric and dataset, the matched method is the highest-scoring baseline after receiving the same receiver-local CNLL calibration; Gap is PACE-CNLL minus its score in percentage points.

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Personalized federated learning (PFL) is one of the important approaches in federated learning for addressing statistical heterogeneity while enabling client-specific adaptation. Many PFL methods split the model into shared and personalized parameters, which are jointly trained on each client. However, this creates an optimization issue: shared parameters are updated by clients optimizing different local objectives, which can lead to inconsistent shared updates and weaken the shared representation. To address this problem, we propose Federated Shared Parameter Correction (FedSPC), a modular correction method for PFL. FedSPC applies control-variate correction only to the shared parameters of a given PFL method, while leaving personalized parameters unchanged. It can be integrated into three common PFL settings: shared feature extractors, shared classifiers, and fully shared models with local regularization. Experiments on CIFAR-100 and Tiny-ImageNet with ViT, ResNet-34, and VGG-11 show that FedSPC improves performance across representative PFL methods, including FedPer, FedRep, FedBABU, LG-FedAvg, and Ditto.

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