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Extending FAIR Principles with Observability for Trustworthy Federated Learning

Hangli Ge, Noboru Koshizuka

arXiv:2610.29252v1cs.LGcs.IR

TL;DR

Federated learning keeps data local, but locality alone does not establish whether a participant’s training conditions are trustworthy. FAIR-O adds continuous observability evidence across infrastructure, data, and trust policies. A two-participant prototype demonstrates evidence generation and assessment, while integration of evidence into participant selection and aggregation remains future work.

  • Problem

    Keeping raw data local does not reveal whether a participant’s service, data pipeline, or policy conditions were trustworthy when its update was produced.

    Source: §I · p. 1

  • Method

    FAIR-O extends FAIR with three observability levels and a Data Space Evidence Plane that manages evidence to support FL decisions.

    Source: §I · p. 1, 4) Evidence Plane · p. 3

  • Results

    Experiment 2 demonstrates FedAvg training over unequal participant quality, while dashboards expose signed evidence and participant conditions without raw telemetry.

    Source: B. Experiment 2: Federated Learning… · p. 4

  • Takeaways & Limitations

    FAIR-O provides evidence-based trust information for data access and FL decisions without exposing raw data or operational telemetry.

    Source: 4) Evidence Plane · p. 3

    The FAIR-O scores do not yet alter automatic participant selection or aggregation weights; those integrations remain future work.

    Source: B. Experiment 2: Federated Learning… · p. 4

Abstract

from arXiv · show

Federated learning (FL) keeps data at its source, but locality alone does not make a federation trustworthy. Without sufficient observability, an aggregator cannot determine whether the client service was healthy, the local data pipeline was trustworthy, or contractual constraints were properly monitored during training. We take the position that trustworthy FL needs an evidence plane in addition to its model-update process. Each participant continuously summarizes local infrastructure, data into minimal, signed trust evidence associated with each FL round, while raw data, logs remain local. A local TinyLLM may assist in interpreting observability signals, deriving contextual evidence, and providing natural-language explanations to operators. We instantiate this vision through FAIR-O, an observability extension of FAIR principle. Rather than benchmarking FL accuracy, a three-level trust model has been proposed and a two-participant Python prototype demonstrates evidence generation and trust assessment. We further discuss future integration of FAIR-O Trust Evidence into participant selection, evidence weighting, and federated aggregation.

I. THE POSITION

Federated learning keeps raw data local, but that alone does not show whether a participant’s update came from a healthy, trustworthy pipeline. FAIR-O proposes continuous, verifiable observability evidence to make the context of updates inspectable.

  • Secure and robust aggregation do not tell operators whether a local training pipeline was observable and trustworthy when its update was produced.This leaves the local conditions behind an update unassessed even when raw data remain centralized nowhere.Source: §I · p. 1
  • Infrastructure, data pipelines, software, and governance change over time, so trustworthiness should be assessed continuously rather than treated as static.Degraded resources, stale or incomplete data, schema changes, provenance gaps, or policy violations may affect update reliability without appearing as attacks.Source: §I · p. 1
  • Without contextual evidence, non-IID behavior may be mistaken for malicious behavior, and FAIR compliance alone may not make runtime quality or policy decisions inspectable.The passage notes that evidence does not solve these problems by itself, but can make context inspectable and trust claims verifiable.Source: §I · p. 1
  • For industrial FL across dataspaces, FAIR-O proposes local TinyLLM assistance because observability signals are heterogeneous and may be sensitive.The TinyLLM can interpret signals and derive contextual evidence while raw data and telemetry remain local; verification and federation operation do not depend on its availability.Source: §I · p. 1
  • FAIR-O adds continuous, verifiable operational evidence to FAIR through infrastructure, data, and trust observability.The levels cover resources and service health; data freshness, completeness, accuracy, lineage, and schema drift; and policy, sovereignty, usage, and provenance monitoring.Source: §I · p. 1

A. FAIR-O Observability Architecture

The FAIR-O architecture separates participant-side observability and evidence generation from the federated-learning workflow. It shares minimized, signed evidence through an Evidence Plane while keeping raw industrial data and telemetry within participant boundaries.

A. FAIR-O Observability Architecture

  • The architecture separates local observability and evidence generation from the federated-learning workflow through a FAIR-O Evidence Plane.The figure describes the plane as managing evidence at the Data Space layer and providing trust information for FL decisions.Source: A. FAIR-O Observability Architecture · pp. 1–2, Figure 1 · p. 2
  • Each provider locally observes infrastructure, data, and trust-policy conditions; deterministic mechanisms generate evidence, with optional local TinyLLM support for querying and explanation.Source: Figure 1 · p. 2
  • Raw industrial data and operational telemetry stay within each participant boundary, while only minimal, signed FAIR-O evidence is exposed to the federation.Source: A. FAIR-O Observability Architecture · p. 2

1) Three Local Observability Levels:

FAIR-O organizes local monitoring into infrastructure, data, and trust-policy observability. Together, the levels describe participant operating conditions, training-data quality, and adherence to expected policies.

1) Three Local Observability Levels:

L1: Infrastructure observability

L2: Data observability

  • L2 data observability tracks freshness, completeness, lineage, and schema consistency, including cases where available participants train on stale or incomplete data.Source: 1) Three Local Observability Levels · p. 2

L3: Trust and policy observability

2) Participant-Side TinyLLM Assistant:

A participant-side TinyLLM can interpret private observability signals locally and explain them to operators. Deterministic components remain responsible for evidence generation, so evidence does not depend on the assistant.

2) Participant-Side TinyLLM Assistant:

  • A TinyLLM can run within a participant’s trusted environment, processing private observability signals without exposing raw data or telemetry to an external LLM service.Source: 2) Participant-Side TinyLLM Assistant · p. 2
  • Operators can query conditions, interpret anomalies, explain policy events, and map observations to FAIR-O dimensions through a natural-language interface.The underlying data remain locally controlled.Source: 2) Participant-Side TinyLLM Assistant · p. 2
  • Deterministic local components handle measurement, filtering, and rule evaluation; TinyLLM is an optional reasoning and explanation layer.FAIR-O evidence remains independently verifiable and available if TinyLLM is unavailable or produces an unreliable explanation.Source: 2) Participant-Side TinyLLM Assistant · p. 2

3) Evidence Generation:

FAIR-O evidence is generated locally from observability signals using data minimization, so raw logs, traces, industrial data, and detailed telemetry are not exported.

3) Evidence Generation:

  • Local observability signals are transformed into FAIR-O evidence before leaving the participant environment, while raw logs, traces, industrial data, and detailed telemetry remain unexported.This follows the principle of data minimization.Source: 3) Evidence Generation · p. 3

4) Evidence Plane:

The Evidence Plane manages and evaluates observability evidence across participants, while the federated-learning layer can use that evidence as context for FL decisions.

4) Evidence Plane:

  • The Evidence Plane stores signed evidence with metadata and provenance, checks it against contracts and usage controls, evaluates trust and risks, and links data products to access conditions.Its components support data-access and FL decisions without exposing raw data or operational telemetry.Source: 4) Evidence Plane · p. 3

5) Integration with Federated Learning:

  • FAIR-O evidence can inform participant selection, round admission, update weighting, anomaly handling, or exclusion when predefined requirements are no longer met.The FL layer otherwise follows the standard initialization, distribution, local training, aggregation, and global-model update workflow.Source: 5) Integration with Federated Learning · p. 3
  • The architecture is intended to make training observable and provide trust information for decisions.Source: 5) Integration with Federated Learning · p. 3

II. PROTOTYPE GROUNDING

The Python prototype uses complementary experiments to demonstrate evidence exchange and executable federated learning, and includes a dashboard view of Participant B’s assets.

II. PROTOTYPE GROUNDING

  • The prototype includes two complementary Python experiments: one for generating, visualizing, and exchanging FAIR-O Trust Evidence without exposing local data, and one for FL with differing participant data quantity and quality.Source: §II · p. 3
  • The Participant-A dashboard presents the federation overview and FAIR-O scores for Participant B’s data assets while keeping raw data unrevealed.Source: Figure 3 · p. 3

A. Experiment 1: FAIR-O Evidence and Visualization

The prototype visualizes exchanged trust evidence and asset-level FAIR-O assessments, alongside a two-participant FL experiment with controlled data degradation.

A. Experiment 1: FAIR-O Evidence and Visualization

  • The prototype uses separate dashboard and evidence-API processes with distinct signing secrets and disjoint participant catalogues, but both TinyLLM clients share one Ollama service on a single host.If Ollama is unavailable, deterministic summaries remain available.Source: A. Experiment 1: FAIR-O Evidence and… · p. 3
  • Figure 4 depicts a two-participant UCI SECOM experiment in which Participant B’s missingness, labels, and feature values are controlledly degraded, alongside sample-count-weighted FedAvg comparisons and a 30-round trajectory.Source: Figure 4 · p. 4
  • Participant A can inspect Participant B’s signed, per-asset evidence, including abstracted FAIR and observability indicators, trust scores, missing fields or telemetry, timestamps, and signatures.The exchanged payload is enough for a local intake decision; raw records, access URLs, telemetry logs, and policy bodies stay with B.Source: A. Experiment 1: FAIR-O Evidence and… · pp. 3–4
  • Participant B’s city-air-quality asset is FAIR-complete but lacks an operational metrics endpoint and correlation identifier, and its infrastructure and trust levels are degraded.Operators can adjust infrastructure, data, and trust evidence weights and inspect their effect on trust scores and FL decisions.Source: A. Experiment 1: FAIR-O Evidence and… · p. 4

B. Experiment 2: Federated Learning Monitoring

The experiments demonstrate FAIR-O monitoring and trust assessment alongside federated training. Experiment 2 runs FedAvg over data with unequal participant quality, while FAIR-O scores do not yet affect participant selection or aggregation weights.

B. Experiment 2: Federated Learning Monitoring

  • The dashboard jointly presents training progress, participant observability, signed evidence, policy compliance, trust indicators, risks, and available data products.It exposes derived evidence rather than raw participant data or telemetry, supporting comparison, anomaly inspection, and audit.Source: B. Experiment 2: Federated Learning… · p. 4
  • Experiment 1 demonstrates local FAIR-O assessment, explainable visualization, evidence minimization, and peer verification.The two experimental paths remain separate; the next step is to bind signed FAIR-O evidence to each FL round and compare ordinary FedAvg with evidence-aware admission and weighting.Source: B. Experiment 2: Federated Learning… · p. 4
  • Experiment 2 demonstrates actual FedAvg training over data with unequal participant quality.The FAIR-O scores do not yet alter participant selection or aggregation weights.Source: B. Experiment 2: Federated Learning… · p. 4

III. CONCLUSION

The conclusion presents FAIR-O as an observability extension for trustworthy federated learning, with local data and logs retained while participants share minimized, signed Trust Evidence. The implemented dashboards demonstrate observability and interactive trust assessment; integrating evidence into participant selection and aggregation remains future work.

III. CONCLUSION

  • FAIR-O extends the FAIR principles with observability for trustworthy federated learning.Participants retain raw data, logs, and policies locally while sharing minimized, signed Trust Evidence.Source: §III · p. 4
  • Deterministic rules can generate evidence, with a local TinyLLM assisting signal interpretation, contextual evidence derivation, and natural-language explanation.Two experimental dashboards demonstrate FAIR-O observability and interactive trust assessment.Source: §III · p. 4
  • Future work will integrate Trust Evidence into participant selection and aggregation and evaluate its effects on accuracy, robustness, fairness, privacy, and overhead.The paper frames this as a shift from static trust toward a continuously observable and verifiable state.Source: §III · p. 4
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