FedRASR: Risk-Aware Soft Reweighting against Persistent Poisoning in Federated Load Forecasting

Federated learning enables collaborative load forecasting without pooling private smart-meter data, but persistent poisoning is difficult to detect because small malicious updates accumulate across rounds and are obscured by regional non-IID variation. To evaluate this threat, we propose Hist-Residual, which repeatedly perturbs profile-aware targets, and Profile-LIE, which additionally shapes uploads using benign round-level coordinate statistics. Existing robust aggregation rules rely on current-round geometry, and cross-round detectors may confuse regional heterogeneity with malicious temporal behavior. To address this, we develop a Federated Risk-Aware Soft Reweighting algorithm (FedRASR), a five-stage server-side defense that forms compact update features, constructs region-calibrated residual trajectories, derives FFT-based frequency evidence, estimates risk, and softly reweights the complete updates. Under the evaluated threat setting, experiments on four regional smart-meter collections with 1,200 clients show that FedRASR attains the lowest RMSE and MAE among the compared methods under both attacks, remains competitive under clean training, and benefits from both frequency scoring and soft reweighting.