Partial Decentralized Gossip-Based Federated Learning Protocol for Heterogeneous IoT Multi-Agents Systems
Bibliographic record
Abstract
We present a decentralized, gossip-based federated learning protocol for heterogeneous Internet of Things multi-agent systems where agents exchange only mutually relevant model components. Each agent partitions its local model into a shared subvector (a subset of parameters) and a private subvector, performs local optimization on the full model. During gossip rounds, agents exchange only the overlapping shared parameters from the shared part of the model with contacted peers using configurable mixing weights, staleness-aware weighting, and optional compression. The protocol supports asynchronous operation, opportunistic connectivity, and open membership with frequent joins and departures. We provide a compact matrix formulation of shared-parameter mixing, derive convergence intuition separating optimization and consensus errors, and identify sufficient connectivity and spectral-gap conditions for mixing on shared subspaces. The approach enables scalable, bandwidth-efficient collaboration for drone swarms, vehicular fleets, and other resource-constrained Internet of Things systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".