FedIA: A Plug-and-Play Importance-Aware Gradient Pruning Aggregation Method for Domain-Robust Federated Graph Learning on Node Classification
This addresses domain robustness in federated graph learning for applications like social networks, though it appears incremental as it builds on existing gradient pruning and aggregation techniques.
The paper tackles the problem of domain skew in Federated Graph Learning (FGL) for node classification, which causes unstable and ineffective aggregation due to noisy gradient signals. The result is that FedIA, a plug-and-play method, achieves smoother convergence and higher accuracy than nine baselines on datasets like Twitch Gamers and Wikipedia, with no extra uplink traffic and negligible server memory.
Federated Graph Learning (FGL) under domain skew -- as observed on platforms such as \emph{Twitch Gamers} and multilingual \emph{Wikipedia} networks -- drives client models toward incompatible representations, rendering naive aggregation both unstable and ineffective. We find that the culprit is not the weighting scheme but the \emph{noisy gradient signal}: empirical analysis of baseline methods suggests that a vast majority of gradient dimensions can be dominated by domain-specific variance. We therefore shift focus from "aggregation-first" to a \emph{projection-first} strategy that denoises client updates \emph{before} they are combined. The proposed FedIA framework realises this \underline{I}mportance-\underline{A}ware idea through a two-stage, plug-and-play pipeline: (i) a server-side top-$ρ$ mask keeps only the most informative about 5% of coordinates, and (ii) a lightweight influence-regularised momentum weight suppresses outlier clients. FedIA adds \emph{no extra uplink traffic and only negligible server memory}, making it readily deployable. On both homogeneous (Twitch Gamers) and heterogeneous (Wikipedia) graphs, it yields smoother, more stable convergence and higher final accuracy than nine strong baselines. A convergence sketch further shows that dynamic projection maintains the optimal $\mathcal{O}(σ^{2}/\sqrt{T})$ rate.