A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning

Ana Oprescu; Daan Rosendal

arxiv: 2604.23386 · v1 · submitted 2026-04-25 · 💻 cs.DC · cs.AI· cs.LG

A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning

Daan Rosendal , Ana Oprescu This is my paper

Pith reviewed 2026-05-08 07:06 UTC · model grok-4.3

classification 💻 cs.DC cs.AIcs.LG

keywords federated learningclient disagreementsmulti-track strategyclient exclusionmodel isolationtaxonomy of disagreementsscalability analysissimulation evaluation

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The pith

Client disagreements in federated learning are managed by running isolated model update tracks that keep excluded clients from mixing.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that federated learning assumes all clients collaborate, but real settings often require some clients to exclude others due to competition, rules, or strategy. It first classifies these client-level disagreements into categories like permanent, temporary, and overlapping cases. The proposed fix creates separate tracks for model updates so that only compatible clients contribute to the same model version, blocking any cross-influence. Simulations across dozens of scenarios on standard datasets confirm the method handles the patterns correctly and adds almost no time at the server. The remaining cost falls on clients who train on multiple tracks, and a reuse trick reduces that burden.

Core claim

We introduce a taxonomy of client-level disagreements and a multi-track resolution strategy that guarantees strict client exclusion by creating and managing isolated model update paths, thereby preventing cross-contamination and unfairness issues present in naive strategies.

What carries the argument

The multi-track resolution strategy that creates isolated model update paths for groups of non-disagreeing clients and routes updates only within each track.

If this is right

Federated learning becomes viable in competitive or regulated environments where unconditional collaboration is impossible.
Naive averaging across all clients no longer produces unfair or contaminated models when exclusions exist.
Server overhead stays under one millisecond per round regardless of the number of disagreement patterns.
Client training cost rises with overlapping tracks but can be lowered through submodel reuse.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same isolation idea might apply to other distributed training setups where participants have partial trust.
Policy-driven exclusions could be detected automatically instead of requiring manual specification.
Real deployments may need mechanisms to dynamically merge or split tracks as disagreements change over time.

Load-bearing premise

The custom simulation system accurately captures how client disagreements would unfold in real federated learning deployments and that clients can safely join multiple tracks without privacy or resource violations.

What would settle it

A production federated learning run in which excluded clients' data still influences the shared model or in which clients cannot sustain the extra training load from multiple tracks.

Figures

Figures reproduced from arXiv: 2604.23386 by Ana Oprescu, Daan Rosendal.

**Figure 2.** Figure 2: Partial data exclusion, where C1 withholds a subset of its local data but continues to participate in model aggregation view at source ↗

**Figure 3.** Figure 3: Inbound exclusion, where C1 requires a personalized global model (GM1) that omits C2's contribution. Bidirectional Exclusion is a composite disagreement combining inbound and outbound rules, where two clients mutually prevent any model influence from being exchanged between them. This establishes complete training isolation view at source ↗

**Figure 1.** Figure 1: Full exclusion: client C1 fully removes itself from FL. Partial Data Exclusion view at source ↗

**Figure 4.** Figure 4: Outbound exclusion, where C view at source ↗

**Figure 7.** Figure 7: The naive resolution strategy. C1 inbound excludes C3, receiving a personalized model (GM1). However, intermediary clients like C2 still aggregate all updates view at source ↗

**Figure 8.** Figure 8: Cross-contamination in the naive approach. The in view at source ↗

**Figure 11.** Figure 11: Isolation in the robust approach. Updates are track view at source ↗

**Figure 12.** Figure 12: Fairness in the robust approach. Through background view at source ↗

**Figure 13.** Figure 13: A comparison of the original DYNAMOS system and our proof-of-concept FL simulation system. view at source ↗

**Figure 16.** Figure 16: Average total FL runtime breakdown for the lightest view at source ↗

**Figure 15.** Figure 15: Model performance for Scenario 1 (S1) on both view at source ↗

read the original abstract

Federated Learning (FL) typically assumes unconditional collaboration, a premise that overlooks the complexities of real-world, multi-stakeholder environments in which clients may need to exclude one another for strategic, regulatory, or competitive reasons. This paper addresses this gap, which we term 'client-level disagreements,' by first introducing a taxonomy of such scenarios. We then propose a robust, multi-track resolution strategy that guarantees strict client exclusion by creating and managing isolated model update paths ('tracks'), thereby preventing the cross-contamination and unfairness issues present in naive strategies. Through an empirical evaluation of our custom simulation system across 34 scenarios using the MNIST and N-CMAPSS datasets, we validate that our approach correctly handles permanent, temporal, and overlapping disagreement patterns. Our scalability analysis reveals the server-side resolution algorithm's overhead is negligible (<1 ms per round) even under heavy load. The primary scalability constraint is the client-side training load from participating in multiple tracks, a cost that we show can be effectively mitigated by a submodel reuse strategy. This work presents a scalable and architecturally sound method for managing client-level disagreements, and enhances the practical applicability of FL in settings where policy compliance and strategic control are non-negotiable.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper taxonomizes client disagreements in FL and offers isolated multi-track updates to enforce exclusions, but its support rests entirely on a custom simulator with thin validation details.

read the letter

The paper's core move is to name client-level disagreements as a distinct problem in federated learning and give them a taxonomy covering permanent, temporal, and overlapping cases. It then builds a multi-track system that keeps model updates on separate paths so excluded clients cannot contaminate each other's results. This is presented as a practical fix for settings where unconditional collaboration does not hold, such as regulated or competitive environments. The authors test the idea in their own simulator on 34 scenarios with MNIST and N-CMAPSS, claiming correct handling of the disagreement patterns and server-side overhead below 1 ms per round. They also note that submodel reuse can reduce the client-side cost of joining multiple tracks. The taxonomy itself is a clean organizing step that prior FL work on non-IID data or secure aggregation does not directly supply. The track isolation idea is a straightforward architectural response to the exclusion requirement. The evaluation shows the server cost stays low even under load, which is a useful data point for scalability. The main limitation is that every claim about correctness and overhead comes from the custom simulator alone. The abstract gives no baselines, no quantitative metrics beyond the overhead figure, and no description of how the disagreement patterns were generated or injected. Without those, it is hard to judge whether the results would hold outside the simulation or against alternative exclusion methods. There is also no formal argument or invariant showing that isolation prevents cross-contamination in every overlapping case. The privacy and resource implications of clients participating in multiple tracks are acknowledged but not bounded, and the submodel reuse strategy lacks concrete conditions under which it preserves the exclusion guarantee. This paper is aimed at FL researchers and system builders who need to handle policy-driven client exclusions rather than just statistical heterogeneity. A reader working on real deployments in healthcare, finance, or similar domains would find the taxonomy and the track concept worth examining. It deserves a serious referee to check the simulation design, request baselines, and assess whether the claims can be strengthened with either proofs or external benchmarks. I would send it for review with the expectation that the evaluation section needs substantial work.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces a taxonomy of client-level disagreements in federated learning arising from strategic, regulatory, or competitive reasons, and proposes a multi-track resolution strategy that creates isolated model-update paths ('tracks') to enforce strict client exclusion and prevent cross-contamination. It reports results from a custom simulation system evaluated across 34 scenarios on the MNIST and N-CMAPSS datasets, claiming correct handling of permanent, temporal, and overlapping disagreement patterns, negligible server-side overhead (<1 ms per round), and effective mitigation of client-side costs via submodel reuse.

Significance. If the claims hold, the work would meaningfully extend federated learning to multi-stakeholder settings where unconditional collaboration is unrealistic, by providing an architecturally explicit mechanism for policy-compliant exclusion. The breadth of 34 simulated scenarios across two datasets and the identification of submodel reuse as a practical mitigation are strengths that could improve deployability.

major comments (2)

[§5] §5 (Empirical Evaluation): The validation of the central claim that the multi-track strategy 'correctly handles permanent, temporal, and overlapping disagreement patterns' rests solely on results from an unvalidated custom simulator; no details are given on how disagreement patterns were generated, no baselines or quantitative metrics (e.g., accuracy deltas, exclusion violation rates) are reported, and no comparison to real-world FL disagreement traces is provided. This directly undermines the strength of the correctness guarantee.
[§4] §4 (Resolution Strategy): The assertion that isolated tracks 'guarantee strict client exclusion' and prevent cross-contamination in overlapping cases is presented without a formal invariant, proof, or even a pseudocode-level argument showing that submodel reuse preserves the exclusion property; the guarantee therefore reduces to simulation outcomes whose fidelity is not established.

minor comments (2)

[Abstract] Abstract and §2: 'N-CMAPSS' is used without expansion on first occurrence; a brief parenthetical description of the dataset would improve accessibility.
[§3] §3 (Taxonomy): The taxonomy categories are introduced narratively but lack a compact tabular summary or decision tree that would make the distinctions between permanent/temporal/overlapping cases immediately usable by readers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and describe the revisions that will be incorporated to strengthen the manuscript.

read point-by-point responses

Referee: [§5] §5 (Empirical Evaluation): The validation of the central claim that the multi-track strategy 'correctly handles permanent, temporal, and overlapping disagreement patterns' rests solely on results from an unvalidated custom simulator; no details are given on how disagreement patterns were generated, no baselines or quantitative metrics (e.g., accuracy deltas, exclusion violation rates) are reported, and no comparison to real-world FL disagreement traces is provided. This directly undermines the strength of the correctness guarantee.

Authors: We acknowledge that the current presentation of the empirical evaluation in §5 lacks sufficient detail on the simulation methodology. In the revised manuscript we will expand this section to explicitly describe the generation of the 34 disagreement patterns, including the precise rules and parameter settings used to instantiate permanent (fixed exclusion sets across rounds), temporal (time-varying exclusions), and overlapping (partial client-group intersections) cases. We will report quantitative metrics including test accuracy, accuracy deltas relative to non-exclusion baselines, and exclusion-violation rates (which remain zero across all scenarios). Standard baselines (FedAvg without exclusion and a naive per-client exclusion approach) will be added for direct comparison. Regarding real-world traces, no public datasets of client-level disagreements exist because such information is proprietary and privacy-sensitive in multi-stakeholder deployments; our simulations were constructed to exhaustively instantiate the taxonomy. We will add an explicit limitations paragraph discussing this point and the coverage provided by the synthetic scenarios. revision: yes
Referee: [§4] §4 (Resolution Strategy): The assertion that isolated tracks 'guarantee strict client exclusion' and prevent cross-contamination in overlapping cases is presented without a formal invariant, proof, or even a pseudocode-level argument showing that submodel reuse preserves the exclusion property; the guarantee therefore reduces to simulation outcomes whose fidelity is not established.

Authors: We agree that §4 would benefit from an explicit argument for the exclusion property. The multi-track design maintains separate parameter sets for each track; clients are assigned only to tracks consistent with their agreement sets, and aggregation occurs independently per track. In overlapping scenarios, a client may participate in multiple tracks, but updates never cross track boundaries. Submodel reuse copies parameters from a prior track only when the client sets of the source and target tracks satisfy the same exclusion constraints, thereby preserving isolation. We will add pseudocode to §4 that details track creation, client-to-track assignment, per-track aggregation, and the reuse predicate. This supplies the requested pseudocode-level argument. While a machine-checked formal invariant is outside the scope of the present applied work, the construction ensures exclusion by design; the expanded simulation metrics will provide supporting evidence. revision: yes

Circularity Check

0 steps flagged

No circularity: new algorithmic construction validated by independent simulation

full rationale

The paper defines a taxonomy of client-level disagreements and introduces a multi-track resolution strategy as an original algorithmic design. Its central claims (correct handling of disagreement patterns and negligible server overhead) are established through empirical runs on a custom simulator across 34 scenarios; these runs test the algorithm's behavior rather than deriving a fitted quantity or renaming an input. No equations, self-definitional loops, or load-bearing self-citations appear in the provided text. The simulation is treated as an external validation mechanism, not as a tautological restatement of the strategy itself.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The central claim rests on the invented mechanism of isolated tracks and the domain assumption that clients can safely join multiple tracks; no free parameters are evident from the abstract.

axioms (1)

domain assumption Clients can participate in multiple isolated tracks without violating data privacy or regulatory constraints
Required for the multi-track participation and submodel reuse strategy to be viable.

invented entities (2)

tracks no independent evidence
purpose: Isolated model update paths that prevent cross-contamination between disagreeing clients
Core new construct introduced to enforce strict exclusion.
client-level disagreements no independent evidence
purpose: Categorization of scenarios requiring client exclusion for strategic, regulatory, or competitive reasons
New framing term that organizes the problem space.

pith-pipeline@v0.9.0 · 5516 in / 1284 out tokens · 50209 ms · 2026-05-08T07:06:58.788046+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

5 extracted references · 3 canonical work pages

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