Power to the Clients: Federated Learning in a Dictatorship Setting

Mohammad Mohammadi Amiri; Mohammadsajad Alipour

arxiv: 2510.22149 · v3 · submitted 2025-10-25 · 💻 cs.LG · cs.AI· cs.CL· cs.CR· cs.CV· cs.DC

Power to the Clients: Federated Learning in a Dictatorship Setting

Mohammadsajad Alipour , Mohammad Mohammadi Amiri This is my paper

Pith reviewed 2026-05-18 04:54 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CLcs.CRcs.CVcs.DC

keywords federated learningmalicious clientsdictator clientsmodel poisoningadversarial attacksconvergence analysisdecentralized training

0 comments

The pith

Dictator clients in federated learning can erase every other client's contributions while keeping their own model intact.

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

The paper introduces dictator clients as a specific class of malicious participants that can nullify all honest updates in the shared model. It supplies explicit attack methods and proves their effect on convergence under both single and multiple dictator scenarios, including cooperation and betrayal. This matters because federated systems rely on aggregation rules that assume roughly equal influence from participants. If the attacks succeed as described, a lone powerful client can effectively dictate the entire learned model without detection.

Core claim

Dictator clients are malicious participants capable of entirely erasing the contributions of all other clients from the server model while preserving their own. Concrete attack strategies achieve this dominance, and theoretical analysis shows the resulting impact on global model convergence when one or several dictators operate independently, collaborate, or eventually betray one another; the claims are backed by experiments on computer vision and natural language processing tasks.

What carries the argument

Dictator client attack, in which a malicious participant crafts updates that dominate a standard aggregation rule such as FedAvg and drive all other clients' influence to zero.

If this is right

The global model converges to a solution determined solely by the dictator client's local data.
When multiple dictators are present, their interactions can produce stable alliances or sudden betrayals that shift the converged model.
Standard aggregation methods without extra safeguards allow complete erasure of honest contributions.
The same dominance pattern appears in both image classification and language modeling benchmarks.

Where Pith is reading between the lines

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

Robust federated systems will need explicit checks that limit any single client's weight in the aggregate update.
The dictator concept may apply to other decentralized training protocols that use similar averaging steps.
Detection of such attacks could rely on monitoring sudden drops in update diversity across rounds.

Load-bearing premise

The server applies an ordinary aggregation rule that lets one client or small group dominate the update without detection or correction.

What would settle it

An empirical run in which the proposed dictator attack is executed yet the final model still shows measurable influence from non-dictator clients' data.

read the original abstract

Federated learning (FL) has emerged as a promising paradigm for decentralized model training, enabling multiple clients to collaboratively learn a shared model without exchanging their local data. However, the decentralized nature of FL also introduces vulnerabilities, as malicious clients can compromise or manipulate the training process. In this work, we introduce dictator clients, a novel, well-defined, and analytically tractable class of malicious participants capable of entirely erasing the contributions of all other clients from the server model, while preserving their own. We propose concrete attack strategies that empower such clients and systematically analyze their effects on the learning process. Furthermore, we explore complex scenarios involving multiple dictator clients, including cases where they collaborate, act independently, or form an alliance in order to ultimately betray one another. For each of these settings, we provide a theoretical analysis of their impact on the global model's convergence. Our theoretical algorithms and findings about the complex scenarios including multiple dictator clients are further supported by empirical evaluations on both computer vision and natural language processing benchmarks.

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

Dictator clients can erase others under raw FedAvg, and the multi-dictator collusion plus betrayal analysis is the freshest element.

read the letter

The paper's main point is that certain malicious clients, which they call dictators, can wipe out all other clients' contributions to the federated model while keeping their own. They also examine cases with multiple dictators who might work together, act separately, or betray each other. This dictator client idea and the breakdown of those multi-client dynamics look new compared to earlier malicious client papers. The authors give concrete attack methods and then analyze convergence for each scenario. They support the theory with experiments on standard computer vision and natural language processing tasks. The analysis builds on typical federated learning assumptions and seems straightforward. The empirical part covers both CV and NLP, which is a plus for showing the attacks are not limited to one domain. The soft spot is the reliance on an undefended server that just does basic averaging. Real federated systems often clip updates or use robust rules to stop any one client from dominating. If those are in place, the dictator would lose some of its power and honest clients would retain influence. The paper does not appear to test against those common mitigations. This is useful for people working on security and robustness in federated learning. A reader interested in how multiple adversaries might interact would get the most out of it. The work is clear enough on its own terms to merit a full review. I would recommend sending it for peer review so that the details of the proofs and the defense question can be checked properly.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces dictator clients as a novel class of malicious participants in federated learning capable of entirely erasing the contributions of all other clients from the server model while preserving their own influence. It proposes concrete attack strategies on standard aggregation rules such as FedAvg, provides theoretical convergence analysis for single-dictator and multi-dictator settings (including collaboration and betrayal scenarios), and supports the findings with empirical evaluations on computer vision and natural language processing benchmarks.

Significance. If the results hold under the stated assumptions, the work is significant for highlighting a severe and analytically tractable vulnerability in standard federated learning. The explicit construction of dictator clients that nullify honest contributions, combined with convergence guarantees across complex multi-party adversarial dynamics, could inform the development of more robust aggregation protocols. The empirical support on CV and NLP tasks adds practical weight to the theoretical claims.

major comments (2)

[Attack Strategies and Convergence Analysis] Attack definition and theoretical analysis sections: The central claim that dictator clients can entirely erase other clients' contributions rests on the assumption that the server applies raw weighted averaging (e.g., FedAvg) without magnitude constraints. Common server-side defenses such as gradient clipping or norm bounding before aggregation would clip scaled updates large enough to nullify honest clients, leaving residual influence and preventing full erasure. This no-defense assumption is load-bearing for both the erasure definition and the subsequent convergence analysis for single and multiple dictators.
[Multiple Dictator Clients] Multi-dictator and betrayal scenarios: The theoretical analysis of collaboration, independent action, and betrayal among dictators appears to inherit the same undefended aggregation assumption. It is unclear whether the tractability and convergence results extend when standard defenses are present, which limits the generality of the findings on complex scenarios.

minor comments (2)

[Abstract] The abstract refers to 'theoretical algorithms'; this appears to be a minor misstatement and should be clarified as theoretical analysis or derivations.
[Experiments] Experimental details on exact attack implementations, scaling factors, and hyperparameter settings for the CV and NLP benchmarks would improve reproducibility and allow verification of the empirical support.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and outline the revisions we will make to improve the manuscript.

read point-by-point responses

Referee: Attack definition and theoretical analysis sections: The central claim that dictator clients can entirely erase other clients' contributions rests on the assumption that the server applies raw weighted averaging (e.g., FedAvg) without magnitude constraints. Common server-side defenses such as gradient clipping or norm bounding before aggregation would clip scaled updates large enough to nullify honest clients, leaving residual influence and preventing full erasure. This no-defense assumption is load-bearing for both the erasure definition and the subsequent convergence analysis for single and multiple dictators.

Authors: We agree that the ability to achieve complete erasure of honest clients' contributions depends on the server performing raw weighted averaging without magnitude constraints or clipping. Our analysis deliberately focuses on this standard FedAvg setting to isolate and highlight a fundamental vulnerability in basic aggregation protocols, which is a common approach in theoretical FL security studies. We will revise the attack definition and analysis sections to explicitly state this modeling assumption and add a discussion of how common defenses such as norm bounding or clipping would prevent full erasure while still permitting dictators to retain disproportionate influence. These changes will clarify the scope without modifying the core theoretical results under the stated assumptions. revision: yes
Referee: Multi-dictator and betrayal scenarios: The theoretical analysis of collaboration, independent action, and betrayal among dictators appears to inherit the same undefended aggregation assumption. It is unclear whether the tractability and convergence results extend when standard defenses are present, which limits the generality of the findings on complex scenarios.

Authors: The multi-dictator analysis, including collaboration, independent action, and betrayal, is developed under the same undefended FedAvg assumption. We acknowledge that introducing defenses would affect the quantitative convergence rates and potentially the tractability of the closed-form results. In the revision we will add explicit remarks on this limitation and provide qualitative observations on how defenses might preserve certain relative power dynamics (e.g., one dictator still dominating after betrayal). A full re-derivation of bounds under clipping is beyond the current scope and would constitute substantial new theoretical work. revision: partial

standing simulated objections not resolved

Full re-derivation of convergence guarantees for single- and multi-dictator settings when standard server-side defenses such as gradient clipping or norm bounding are applied

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained

full rationale

The paper defines dictator clients as a novel class capable of erasing other contributions under standard aggregation (e.g., FedAvg) and derives theoretical convergence results for single/multiple dictator scenarios directly from the attack model and aggregation equations. No steps reduce by construction to fitted inputs, self-citations, or renamed known results; the analysis follows standard FL convergence techniques applied to the explicitly stated attack strategies, with empirical support on CV and NLP benchmarks providing independent verification. The central claims remain independent of the inputs rather than tautological.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the newly introduced dictator client definition and the assumption that standard aggregation permits complete erasure of other contributions. No free parameters are mentioned. One domain assumption about FL convergence is used for the theoretical analysis.

axioms (1)

domain assumption Standard federated learning convergence assumptions under aggregation rules such as FedAvg
Invoked to analyze impact on global model's convergence for the dictator scenarios.

invented entities (1)

dictator clients no independent evidence
purpose: Model a class of malicious participants that can dominate and erase other clients' contributions
Newly postulated entity introduced to make the attack analytically tractable; no independent evidence outside the paper is provided.

pith-pipeline@v0.9.0 · 5719 in / 1264 out tokens · 43752 ms · 2026-05-18T04:54:27.060476+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

M_t = ∇L_m(ˆθ_m_t) − (ˆθ_m_{t−1} − θ_t / η − ∇L_m(ˆθ_m_{t−1})) (Eq. 6, Algorithm 1)
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

θ_{t+1} = ˆθ_m_t − η (∇L_m(ˆθ_m_t) + Σ_{n≠m} ∇L_n(θ_t)) (derivation after Eq. 6)

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.