# Gradient Manipulation in Distributed Stochastic Gradient Descent with Strategic Agents: Truthful Incentives with Convergence Guarantees

arXiv: https://arxiv.org/abs/2603.27962 · HTML: https://mlsanity.com/paper/2603.27962/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 56 references that are in the map.
- First posted on arXiv 30 Mar 2026.

## Builds on (most cited first, 26 of 26)

- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1705.09056/) (2017)
- [A Survey on Distributed Machine Learning](https://mlsanity.com/paper/1912.09789/) (2019)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Decentralized Deep Learning with Arbitrary Communication Compression](https://mlsanity.com/paper/1907.09356/) (2019)
- [Distributed Stochastic Gradient Tracking Methods](https://mlsanity.com/paper/1805.11454/) (2018)
- [FedLab: A Flexible Federated Learning Framework](https://mlsanity.com/paper/2107.11621/) (2021)
- [A Unified and Refined Convergence Analysis for Non-Convex Decentralized Learning](https://mlsanity.com/paper/2110.09993/) (2021)
- [Optimum Statistical Estimation with Strategic Data Sources](https://mlsanity.com/paper/1408.2539/) (2014)
- [Mechanism Design for Collaborative Normal Mean Estimation](https://mlsanity.com/paper/2306.06351/) (2023)
- [Social Media and Misleading Information in a Democracy: A Mechanism Design Approach](https://mlsanity.com/paper/2003.07192/) (2020)
- [Distributed Optimization Methods for Multi-Robot Systems: Part II -- A Survey](https://mlsanity.com/paper/2301.11361/) (2023)
- [Network Utility Maximization based on Incentive Mechanism for Truthful Reporting of Local Information](https://mlsanity.com/paper/1909.03938/) (2019)
- [Collaborative Mean Estimation Among Heterogeneous Strategic Agents: Individual Rationality, Fairness, and Truthful Contribution](https://mlsanity.com/paper/2407.15881/) (2024)
- [Private and Truthful Aggregative Game for Large-Scale Spectrum Sharing](https://mlsanity.com/paper/1608.05537/) (2016)
- [Stochastic Averaging for Constrained Optimization with Application to Online Resource Allocation](https://mlsanity.com/paper/1610.02143/) (2016)
- [Distributed Variable Sample-size Stochastic Optimization with Fixed Step-sizes](https://mlsanity.com/paper/2108.05078/) (2021)
- [A Dual Accelerated Method for Online Stochastic Distributed Averaging: From Consensus to Decentralized Policy Evaluation](https://mlsanity.com/paper/2207.11425/) (2022)
- [Distributed Stochastic Optimization under Heavy-Tailed Noises](https://mlsanity.com/paper/2312.15847/) (2023)
- [Incentivizing Truthful Collaboration in Heterogeneous Federated Learning](https://mlsanity.com/paper/2412.00980/) (2024)
- [Locally Differentially Private Gradient Tracking for Distributed Online Learning over Directed Graphs](https://mlsanity.com/paper/2310.16105/) (2023)
- [Distributed Stochastic Zeroth-Order Optimization with Compressed Communication](https://mlsanity.com/paper/2503.17429/) (2025)
- [Approximately Truthful Multi-Agent Optimization Using Cloud-Enforced Joint Differential Privacy](https://mlsanity.com/paper/1509.08161/) (2015)
- [Incentive Compatibility in Stochastic Dynamic Systems](https://mlsanity.com/paper/1901.01978/) (2019)
- [Incomplete Information VCG Contracts for Common Agency](https://mlsanity.com/paper/2105.14998/) (2021)
- [Ensuring Truthfulness in Distributed Aggregative Optimization](https://mlsanity.com/paper/2501.08512/) (2025)

## Abstract (arXiv)

Distributed learning has gained significant attention due to its advantages in scalability, privacy, and fault tolerance.In this paradigm, multiple agents collaboratively train a global model by exchanging parameters only with their neighbors. However, a key vulnerability of existing distributed learning approaches is their implicit assumption that all agents behave honestly during gradient updates. In real-world scenarios, this assumption often breaks down, as selfish or strategic agents may be incentivized to manipulate gradients for personal gain, ultimately compromising the final learning outcome. In this work, we propose a fully distributed payment mechanism that, for the first time, guarantees both truthful behaviors and accurate convergence in distributed stochastic gradient descent. This represents a significant advancement, as it overcomes two major limitations of existing truthfulness mechanisms for collaborative learning:(1) reliance on a centralized server for payment collection, and (2) sacrificing convergence accuracy to guarantee truthfulness. In addition to characterizing the convergence rate under general convex and strongly convex conditions, we also prove that our approach guarantees the cumulative gain that an agent can obtain through strategic behavior remains finite, even as the number of iterations approaches infinity--a property unattainable by most existing truthfulness mechanisms. Our experimental results on standard machine learning tasks, evaluated on benchmark datasets, confirm the effectiveness of the proposed approach.