Yuxuan Sun — communication-efficient federated learning over wireless and vehicular edge networks
2
papers here
2
reviewed by Evolve
1
first-author
0
corresponding
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works · OpenAlex
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h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-09-20) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.
Research profile
Output—
The output axis is unscored: no verified publication count, venue list, or indexing record is available in this profile's data scope. It would measure total scholarly production — the number of papers published, the venues they appeared in, and how that volume is distributed over time.
Impact—
The impact axis is unscored: no citation counts, h-index, or other influence metrics exist in the input data. It would measure community uptake — citations, downstream reuse, and recognition of this work within federated learning and wireless edge research.
Focus0/100
Focus scores 0/100, mirroring the raw field distribution, which splits exactly evenly — 0.5 to machine learning (federated learning and wireless edge systems) and 0.5 to wireless communications and machine learning (communication-efficient federated learning / vehicular edge intelligence). The two reviewed papers pull toward neighboring directions: FedPGT optimizes gradient transport across time-varying vehicular channels, while Sylvas schedules devices against continual-learning drift, so no single concentrated line dominates yet.
Collaboration46/100
The collaboration axis scores 46/100, drawn from 9 distinct co-authors in the raw record. The cluster is tight and recurrent: Tan Chen, Sheng Zhou, Zhisheng Niu, Jintao Yan, and Yuxuan Bai appear repeatedly across the co-author list. Authorship sits at first position on Sylvas and in a middle slot on FedPGT, with neither paper flagged as corresponding — a group-anchored, early-career pattern rather than wide external networking.
Activity—
The activity axis is unscored: the snapshot covers only two 2026 arXiv preprints, too thin to compute a yearly publication cadence. It would measure research tempo — output per year and sustained publishing streaks across the career timeline.
Reproducibility38/100
Reproducibility averages 38/100 across the 2 papers with readiness assessments (reproMean 38, reproPapers 2). FedPGT rates medium — a coherent theory-to-algorithm pipeline from Lyapunov-style scheduling to progressive gradient transmission with simulation gains, but no variance reporting and narrow sparsification baselines. Sylvas rates low — a sound magazine-style scheduler with thin, simulation-only evaluation. Releasing code and adding variance-aware, broader-baseline experiments would most directly lift this axis.
Research summary
Yuxuan Sun is an early-stage researcher working at the interface of federated learning and wireless edge systems; this profile is built from two 2026 arXiv preprints reviewed in the Evolve catalog, since no citation, output, or career metadata is in scope. The raw field distribution splits evenly — 0.5 to machine learning (federated learning and wireless edge systems) and 0.5 to wireless communications and machine learning (communication-efficient federated learning / vehicular edge intelligence) — so the work reads as two neighboring lines rather than one. FedPGT (middle authorship) progressively uploads dominant gradient entries slot by slot over time-varying channels, coupling Lyapunov-style resource scheduling with convergence analysis; Evolve's review credits a coherent theory-to-algorithm pipeline with simulation gains while flagging missing variance reporting and narrow sparsification baselines. Sylvas (first authorship) frames device scheduling in federated continual learning as a synergistic learning value over client drift, collective divergence, and pseudo-label usefulness — a magazine-style concept judged sound but thinly evaluated in simulation only. Collaboration is group-anchored: 9 distinct co-authors with Tan Chen, Sheng Zhou, Zhisheng Niu, Jintao Yan, and Yuxuan Bai recurring, scoring 46/100; reproducibility averages 38/100 across the two papers.
Collaboration pattern
The co-author set is small and stable: 9 distinct collaborators, with Tan Chen, Sheng Zhou, Zhisheng Niu, Jintao Yan, and Yuxuan Bai recurring in the list. Both 2026 preprints come from the same cluster — Sylvas with first authorship, FedPGT in a middle slot, and neither flagged as corresponding — suggesting a group-anchored, early-career pattern rather than wide external networking; the collaboration axis scores 46/100.
Research keywords
Highlights
FedPGT: progressive gradient transmission for vehicular federated learning
Sylvas: synergistic learning value for device scheduling in federated continual learning
A theory-to-system line across two 2026 preprints
Together the two papers trace one theme — keeping federated learning workable under wireless constraints — spanning channel-limited gradient transport and forgetting-aware device scheduling; both remain at the simulation stage, and the pair sets the current reproducibility mean of 38/100.
Timeline
- 2026FedPGT preprint posted to arXiv (middle authorship): progressive gradient transmission for vehicular federated learning over time-varying channels, with Lyapunov-style scheduling and convergence analysis.
- 2026Sylvas preprint posted to arXiv with first authorship: synergistic-learning-value device scheduling for federated continual learning at the edge.
- 2026Both preprints enter the Evolve catalog in the same year, forming the current profile basis: 2 reviewed papers, a 38/100 reproducibility mean, and a 50/50 field split across two adjacent directions.
Papers in the Evolve catalog
Frequent co-authors
About this profile
This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.
- Bibliometrics come from OpenAlex-based public bibliographic data only; no citation counts, h-index, or venue-level metrics are in scope.
- Identity is name-resolved from public bibliographic data; 'Yuxuan Sun' is a common name and the same name also appears in the co-author list, so disambiguation is not fully verified. No institution, country, or ORCID is available.
- Only two 2026 arXiv preprints were reviewed; the output, impact, and activity axes lack sufficient underlying data and are intentionally left unscored.
- The reproducibility score rests on 2 assessed papers with simulation-only evidence; no public code artifacts were verified.