{"id":"9769c321-43da-4251-a816-1f28edac70c4","arxiv_id":"2608.09082","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":6,"one_line_summary":"F2STNet combines truncated graph Fourier features, a diagonal state-space temporal layer, and fairness-aware federated aggregation to improve graph forecasting accuracy and client fairness.","lead":"This paper introduces F2STNet, a federated graph forecasting model that combines graph Fourier features, a lightweight state-space temporal encoder, and loss-based client reweighting to improve accuracy and fairness on distributed traffic and environmental data. It reports small but consistent accuracy gains over existing baselines on three public datasets, with better worst-client and dispersion metrics in federated training.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Headline comparisons lack paired baseline variance and a stated common backbone for FL methods; the reported RMSE/fairness advantages may not be statistically reliable.","rationale":"I focused on the central empirical claim. The strongest condition for it to hold is that the reported gains reflect the method and are not artifacts of unequal setup or noise. The reader's weakest assumption (shared global adjacency) is a genuine scope limitation, but the formulation in Section 3.1 is internally consistent if clients receive time-partitioned data over the same N nodes; it does not by itself falsify the PeMS04 experiments. The missing common-backbone statement and absent baseline variance are more decisive because they affect exactly the two claims made in the abstract. A conditional verdict is appropriate: the concerns are checkable and the required experiments are standard. I therefore leave the verdict unchanged.","tokens_in":15856,"tokens_out":10809,"duration_ms":110455,"concrete_test":"Obtain the federated partition code and rerun the eight FL aggregation baselines with the exact F2STNet backbone, the same hyperparameters, and the same three paired seeds used for F2STNet; then compute per-seed Max-RMSE, Std-RMSE, and Δ (Eq. 20) and run paired permutation tests for F2STNet versus FedAvg and versus FGGP. Also rerun the Table 1 centralized baselines with the same three seeds and report paired RMSE/MAE differences with confidence intervals; if any claimed advantage has overlapping intervals or p>0.05, the headline superiority/fairness claims should be weakened or made dataset-specific.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim (Section 1) is that F2STNet 'consistently outperforms SOTA baselines in spatiotemporal prediction across all datasets, and achieves superior fairness under federated settings on PeMS04.' The evidence for both parts is vulnerable at the same point: the comparison protocol is underspecified. Section 4.2 lists FedAvg, FedProx, MOON, FedOPT, FedProc, FedSage, FedProto, and FGGP as 'aggregation strategies' but never states which backbone carries them; if the backbone differs from F2STNet, the federated accuracy and Max-RMSE/Std-RMSE gaps in Table 2 measure architecture, not the proposed FFA aggregation. Further, Section 4.4 says 'standard deviations are displayed only for configurations whose complete set of paired runs has been verified,' yet Tables 1 and 2 report mean±std only for F2STNet; baseline rows are single numbers. Eq. (17) defines paired-seed statistics but no paired differences, confidence intervals, or tests are reported. Without those, the 0.3-point RMSE lead on HZMetro and the 0.44-unit Std-RMSE lead on PeMS04 could be within run-to-run noise. The shared-graph assumption (Section 3.1) is a real applicability limit, but the more decisive defect is that the evaluation cannot distinguish aggregation gains from backbone advantages or chance. The paper's own admission that FFA is 'sensitive to noisy validation losses and outlier clients' (Section 6) makes per-seed variance in fairness metrics especially important.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"This paper proposes F2STNet, a federated spatiotemporal forecasting model that combines truncated graph-Fourier spectral projection, a diagonal state-space temporal encoder, graph convolution, and a fairness-aware federated aggregation scheme (FFA). FFA reweights client updates using validation losses under a linearly increasing fairness schedule. The authors report centralized experiments on PeMS04, HZMetro, and KnowAir and federated experiments on PeMS04, with additional HZMetro and KnowAir results in an appendix, claiming consistent state-of-the-art forecasting accuracy and improved client-level fairness.","tokens_in":16161,"tokens_out":9348,"duration_ms":87469,"significance":"If the claims hold, the paper offers a compact architecture with sub-1.2M parameters, linear temporal complexity, and a simple, testable fairness mechanism. The model equations are internally coherent, the shared-topology assumption is explicitly stated, and the ablations and sensitivity analyses are useful for understanding the design. The main value of the contribution is the combination of known components, rather than a fundamentally new modeling principle; nevertheless, the reported efficiency numbers and the explicit client-level fairness metrics are potentially useful for practitioners. The evaluation protocol, however, currently does not provide enough statistical evidence to support the headline claims of consistent outperformance and superior fairness.","major_comments":[{"comment":"Section 4.4 states that the same seed list is used for all compared methods so that differences can be analyzed with paired runs, and Eq. (17) defines paired-seed means and standard deviations, but Tables 1 and 2 report standard deviations only for F2STNet, and no paired differences, confidence intervals, or tests are presented. This matters directly for the headline claim: on HZMetro in Table 1, F2STNet's RMSE is 29.213±0.15 versus 29.232 for STG-Mamba, and its MAE is 18.262±0.08 versus 18.264, so the advantage is smaller than the reported run-to-run spread. In Table 2, the Std-RMSE improvement of 0.75 versus 0.81 is a single number with no seed-to-seed variability. Please report baseline standard deviations, paired per-seed differences for the key metrics, and a significance test or effect-size measure, or explicitly soften the 'consistently outperforms SOTA baselines' claim.","section":"§4.2 / Table 2"},{"comment":"The federated baselines (FedAvg, FedProx, MOON, FedOPT, FedProc, FedSage, FedProto, FGGP) are described as 'aggregation strategies,' but the backbone model carrying these strategies is never stated. If the backbone is not F2STNet's spectral+state-space network, the accuracy and fairness gaps in Table 2 measure a mixture of architecture and aggregation effects, and the superiority of FFA over the compared aggregation methods is not established. The paper should specify the common backbone and hyperparameters used for all federated baselines, or explicitly present the comparison as full-method comparisons and include a F2STNet + vanilla FedAvg control (the 'w/o Fair Aggregation' ablation is a step in this direction but needs to be placed alongside the baselines in the same table).","section":"§3.1 / §1"},{"comment":"The problem formulation assumes a single globally shared adjacency matrix A∈R^{N×N}, and the truncated graph-Fourier basis U_F is precomputed once from this shared topology. Consequently, all clients must observe the same N nodes with the same connectivity, and the federated partition is only over time samples or records. The introduction's examples ('data silos, such as distributed traffic sensors') suggest a different and more general scenario in which clients own disjoint sensors or distinct graphs; for that scenario the spectral projection and graph convolution in Eqs. (1)–(8) are undefined. Please state the shared-topology assumption in the contributions and abstract, and discuss whether or how the method extends to disjoint-client graphs. This limitation does not invalidate the PeMS04 experiments, but it materially narrows the claimed scope of 'decentralized and heterogeneous data.'","section":"§4.3 / §4.7"}],"minor_comments":[{"comment":"The sentence 'the gains on HZMetro demonstrate the benefit of fairness-aware aggregation for periodic graph structures' is confusing because the HZMetro results in Table 1 are centralized; please clarify whether this refers to the federated HZMetro results in Appendix B.","section":"§4.3"},{"comment":"The baseline name alternates between 'STAFormer' and 'STAEformer'; please standardize. Reference [30] for FedOPT is cited as ICLR 2021, but the listed source is an arXiv preprint; please correct the venue.","section":"§4.2 / Table 1"},{"comment":"The caption says the variants are compared on all three datasets, but 'w/o Fair Aggregation' is a federated-only modification; please specify the configuration used for the centralized datasets so the reader knows what 'w/o Fair' means there.","section":"Figure 5"},{"comment":"The sensitivity analysis is described as heatmaps for λ_init, λ_max, and η, but the numerical values are not reported in the text; please include a table or numeric summary so the stability claim can be verified.","section":"Figure 6 / §4.7"},{"comment":"The text reports the 'best observed configuration' without stating whether the hyperparameter search was performed on a validation split; please clarify this to rule out test-set tuning of λ_init, λ_max, η, F, and K.","section":"§4.7"},{"comment":"No code or data availability statement is provided; please add a reproducibility statement or specify whether code will be released.","section":"All"}],"recommendation":"major_revision","confidential_remarks":"The manuscript is internally consistent and the central derivation is coherent; the main obstacle is the evaluation protocol, which needs baseline variance, paired statistics, and a stated common backbone before the headline claims can be accepted. The shared-global-graph assumption should be foregrounded as a scope condition. I do not see a need to reject; the required revisions are within the scope of a major revision."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take: this is a reasonable paper that combines known components in a new way. The spectral projection onto a truncated graph Fourier basis, the diagonal state-space scan, and the GCN are all established, but putting them together with the FFA reweighting (Eqs. 13–16) is new, and the reverse-annealing lambda schedule is a small, clearly described mechanism. The ablation is informative: dropping the temporal module hurts the most, and removing spectral or fairness components degrades results, which supports the design choices.\n\nThe math is coherent. The FFA rule guarantees positive weights, the discretization in Eqs. (4)–(7) is standard, and the efficiency story is credible (under 1.2M parameters, O(T) temporal complexity). For a federated setting, the communication cost and the simplicity of the aggregation matter, and the paper makes that case.\n\nThe soft spots are in the evaluation, and they are not minor. The central claim is 'consistently outperforms SOTA baselines,' but on HZMetro the RMSE gap over STG-Mamba is 0.019 (29.213 vs 29.232) while F2STNet's own standard deviation is 0.15. That difference is noise. Baseline rows in Tables 1 and 2 are single numbers, even though Section 4.4 says the same seed list was used for all methods and paired differences could be analyzed. No paired differences, confidence intervals, or tests are reported. If the authors have the per-seed numbers, they should show them.\n\nThe federated comparison has a more basic omission: Section 4.2 lists FedAvg, FedProx, MOON, etc. as 'aggregation strategies' but never states the backbone. If all FL baselines use F2STNet as the backbone, that needs to be explicit; otherwise the accuracy and fairness gaps in Table 2 could be architecture effects rather than aggregation effects. The fairness metrics (Max-RMSE, Std-RMSE) are also single numbers with no variance, which is especially important given the paper's own admission that FFA is sensitive to noisy validation losses and outlier clients.\n\nThe shared-graph assumption (a single global adjacency matrix across clients) is a real applicability limit, but it's a stated assumption, not a hidden one. I would not call it fatal, though the paper should acknowledge that it does not cover cross-city or client-specific graph settings.\n\nThe hyperparameter selection (F, K, lambda schedule) is described as 'selected empirically' without specifying a validation-based protocol, so test-set tuning cannot be ruled out. That is a standard issue in this literature, but the paper should say how it chose these values.\n\nBottom line: the architecture is sensible and the fairness mechanism is a legitimate contribution. The paper is seriously flawed on evidence reporting but not on conception. A competent referee could get it into reasonable shape with a request for baseline variance, an explicit backbone statement, and a validation-based hyperparameter protocol. I would send it out.","headline":"Coherent hybrid model with a sensible fairness aggregation, but the empirical evidence is thinner than the claims suggest—baseline variance and the FL backbone need to be reported.","tokens_in":16724,"tokens_out":4279,"would_cite":true,"duration_ms":36597,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"F2STNet claims that a single federated architecture—combining a truncated graph-Fourier basis, a diagonal state-space temporal encoder, and loss-aware client reweighting—achieves both better forecasting accuracy and better client-level…","keywords":["federated learning","spatiotemporal forecasting","graph neural networks","graph Fourier transform","state-space models","fairness-aware aggregation","traffic forecasting","air quality forecasting"],"falsifier":"Run FFA on a fixed PeMS04 split but replace each client's validation loss with a noisy surrogate matched in mean and variance to the true loss. If worst-client MAE and client-wise standard deviation stop improving over FedAvg, then FFA's reported fairness gains depend on validation loss reliability, a sensitivity the paper itself acknowledges.","tokens_in":15638,"feed_emoji":"🚦","tokens_out":12568,"duration_ms":94471,"temperature":0.7,"pith_summary":"F2STNet is a single federated architecture that forecasts graph-structured time series by combining a truncated graph-Fourier basis, a diagonal state-space temporal encoder, and fairness-aware client aggregation. The paper's central claim is that this combination beats the compared baselines on three real-world benchmarks (best RMSE, MAE, and MAPE on PeMS04 and KnowAir; best MAE on HZMetro) and, under federated splits of PeMS04, is more equitable across clients in worst-case error and client-to-client dispersion. If true, it means that privacy-preserving, resource-constrained forecasting can get both accuracy and fairness from one compact model instead of having to choose between them.","feed_headline":"F2STNet beats forecasting baselines and improves worst-client fairness","feed_subtitle":"Spectral features, a state-space encoder, and fairness-aware aggregation improve forecasts and reduce client disparity.","key_machinery":"The central carrying object is the truncated graph-Fourier basis $U_F \\in \\mathbb{R}^{N\\times F}$, built from the eigendecomposition of the normalized graph Laplacian with self-loops $\\tilde{L} = I - \\tilde{D}^{-1/2}\\tilde{A}\\tilde{D}^{-1/2} = U\\Lambda U^\\top$. Keeping $F$ eigenvectors and forming $S_{b,t,n,f} = (U_F)_{n,f} (U_F^\\top X)_{b,t,f}$ gives each node a frequency-coordinate vector that a shared MLP embeds, letting the model see local and global graph-frequency structure without learning the projection. The other key piece is the diagonal state-space temporal layer: for each node, a per-step scan $h_t = \\bar{A}_t \\odot h_{t-1} + \\bar{B}_t \\odot z_t$ with $\\bar{A}_t = \\exp(\\Delta_t \\odot A)$ and $\\bar{B}_t = (\\bar{A}_t - 1) \\oslash A \\odot B_t$, outputting only the final state. This gives $O(T)$ temporal complexity, and the ablation shows it is the component whose removal hurts most. FFA's weighting $q_k^t = p_k^t(1 + \\lambda_t r_k^t)$, with $\\lambda_t$ increasing under a reverse-annealing schedule, is the fairness mechanism.","core_discovery":"F2STNet claims that spectral and state-space temporal modeling plus loss-based client reweighting deliver both accuracy and fairness in federated graph forecasting. The spectral branch projects each node's signal onto a truncated graph-Fourier basis $U_F$ precomputed from the shared normalized graph Laplacian, giving every node a frequency profile that a shared MLP embeds; the temporal branch runs a diagonal state-space layer with input-dependent parameters over each node's sequence, using only the final latent state; a graph convolution then mixes spatial neighborhoods, and a linear head produces the $H$-step forecast. In the federated setting, FFA replaces FedAvg's sample-count weights with weights adjusted by relative validation-loss deviation, scaled by an annealing coefficient that grows over rounds. The paper reports that F2STNet achieves the best RMSE, MAE, or MAPE on PeMS04 and KnowAir, the best MAE on HZMetro, and the best composite utility-fairness score on federated PeMS04, with lower worst-client RMSE and lower client-wise standard deviation than all compared aggregation methods.","pith_inferences":["The method assumes every client shares the same node set and adjacency; a natural extension would learn a shared spectral basis across heterogeneous graphs or align local Laplacians, which the paper leaves open.","The reverse-annealing schedule is linear; a history-aware aggregator (for instance, an exponential moving average of validation losses) would likely be more robust to noisy loss signals, a sensitivity the conclusion itself flags.","On KnowAir, where the paper reports only marginal federated gains, the correlation between validation loss and client difficulty is probably weaker; FFA's advantage would be expected to grow with the reliability of the loss signal.","A direct test of FFA's mechanism: perturb the validation losses with random noise on a PeMS04 split; if worst-client MAE stops improving over FedAvg, the benefit comes from the loss signal rather than from the weighting formula alone."],"forward_implications":["If the accuracy claims hold, federated traffic and environmental forecasting can drop heavy recurrent or attention modules: the diagonal state-space layer already covers long-range dependence at linear cost.","FFA-style reweighting could serve as a drop-in replacement for FedAvg in other federated regression tasks, improving worst-client performance without adding protected attributes or a separate fairness objective.","Because the graph-Fourier basis is precomputed once from shared topology, the spectral part adds no per-round communication or runtime overhead, so the efficiency gain transfers directly to bandwidth-constrained deployments.","The reported gains across traffic, metro, and air-quality data suggest the architecture is not tied to one sensor modality, though the federated analysis centres on PeMS04.","The composite utility-fairness score $\\Delta$ defined in the paper offers a single number that captures both average accuracy and client disparity, which could become a standard reporting metric for federated forecasting."],"supporting_citations":[{"why":"Supplies the spectral graph Fourier method that the spectral branch builds on.","marker":"[2]"},{"why":"Provides the selective state-space model that the temporal encoder adapts.","marker":"[17]"},{"why":"Establishes fairness-aware reweighting of client updates, which FFA extends and compares against.","marker":"[9]"},{"why":"Defines FedAvg, the baseline aggregation that FFA modifies and the main comparison point in the federated tables.","marker":"[25]"},{"why":"A recent federated graph-learning method whose reported results form a key baseline in the federated comparison.","marker":"[33]"}],"fun_headline_variants":["F2STNet: spectral-temporal modeling for fair federated forecasting","Fair federated graph forecasting with spectral-temporal F2STNet","F2STNet improves accuracy and worst-client fairness in federated forecasting","Spectral-temporal federated model boosts graph forecast fairness","F2STNet: better forecasts, fairer clients in federated graph learning"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that every federated client observes the same set of $N$ nodes with the same adjacency matrix, because the graph-Fourier basis and the graph convolution are global and precomputed once; if clients hold different sensors or different road networks, the spectral projection and convolution cannot be applied as written.","fun_headline_variants_meta":{"raw":{"variants":["F2STNet: spectral-temporal modeling for fair federated forecasting","Fair federated graph forecasting with spectral-temporal F2STNet","F2STNet improves accuracy and worst-client fairness in federated forecasting","Spectral-temporal federated model boosts graph forecast fairness","F2STNet: better forecasts, fairer clients in federated graph learning"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000164,"raw_usage":{"total_tokens":1227,"prompt_tokens":904,"completion_tokens":323,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":520,"completion_tokens_details":{"reasoning_tokens":232}},"tokens_in":520,"tokens_out":323,"duration_ms":3503,"temperature":1.0,"reasoning_tokens":232,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-11T23:52:43.693364+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run FFA on a fixed PeMS04 split but replace each client's validation loss with a noisy surrogate matched in mean and variance to the true loss. If worst-client MAE and client-wise standard deviation stop improving over FedAvg, then FFA's reported fairness gains depend on validation loss reliability, a sensitivity the paper itself acknowledges.","supporting_citations":[{"cited_title":"Spectral temporal graph neural network for multivariate time-series forecasting","cited_arxiv_id":null,"evidence_quote":"Supplies the spectral graph Fourier method that the spectral branch builds on."},{"cited_title":"Ezzeldin, Shen Yan, Chaoyang He, Emilio Ferrara, and Salman A","cited_arxiv_id":null,"evidence_quote":"Establishes fairness-aware reweighting of client updates, which FFA extends and compares against."},{"cited_title":"Communication- Efficient Learning of Deep Networks from Decentralized Data","cited_arxiv_id":null,"evidence_quote":"Defines FedAvg, the baseline aggregation that FFA modifies and the main comparison point in the federated tables."},{"cited_title":"Federated graph learning under domain shift with generalizable proto- types","cited_arxiv_id":null,"evidence_quote":"A recent federated graph-learning method whose reported results form a key baseline in the federated comparison."}],"review_version":1}