Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners

arXiv:2505.12010 · cs.GT, cs.LG, cs.MA · Submitted 2025-05-17 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "Incentivize Contribution and Learn Parameters Too".

Jane: The gist: This paper addresses simultaneous parameter learning and incentivizing contribution in a truthful manner in federated learning through two mechanisms, UPBReD and 2P-UPBReD,

Tom: First, who's behind it and why it matters.

Title and authors: Tom: So we're talking about this paper called "Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners." Jane, what's the core problem they're trying to fix here?

Jane: Well, Tom, it starts with classical federated learning where clients have limited data and they voluntarily share it. But the authors point out that this doesn't account for why an agent would actually choose to participate and contribute their data, especially when collecting that data is costly for them.

Tom: Exactly. The problem is that if contribution costs something, rational agents might try to free-ride, just contributing the bare minimum and learning off everyone else’s data, which disincentivizes honest participation <ref:2505.12010#pg1>.

Lu: The paper tackles this by looking at two mechanisms. They first propose something called Updated Parameter Best Response Dynamics, or UPBReD, which aims to let agents learn the model parameters and contribute their data at the same time <ref:2505.12010#pg3>.

Meng: So they’re trying to get simultaneous learning and contribution going. But what’s the catch? They say this initial mechanism might result in sub-optimal contribution by the agents, which means the global learning also ends up being sub-optimal <ref:2505.12010#pg3>.

Jane: And that’s where they introduce payments. They add a payment rule to UPBReD to make sure that every agent reports their data truthfully in every round <ref:2505.12010#pg3>. This is called T-UPBReD, and it shows they can achieve EPIC in every round <ref:2505.12010#pg3>.

Tom: EPIC means something specific about the incentive properties, right? It means truthful reporting holds even with these payments <ref:2505.12010#pg3>. But they also mention a second phase, 2P-UPBReD, which is where they really push for full contribution <ref:2505.12010#pg3>.

Lu: Two Phase Updated Parameter Best Response Dynamics, or 2P-UPBReD, is designed to learn the optimal parameters for all agents and then use monetary transfers to incentivize every agent to contribute their maximum amount of data <ref:2505.12010#pg3>.

Meng: So in this second phase, they are forcing full contribution while also learning the optimal model parameters, and they claim it converges to the Nash equilibrium where everyone contributes their maximum amount of data <ref:2505.12010#pg3>.

Jane: That's a big deal for practical deployment because it addresses that free-riding issue directly by structuring the incentives in a way that encourages maximum contribution <ref:2505.12010#pg3>.

Title and authors: Tom: And they show robust convergence to the optimal model parameters, w OPT, under Assumptions three and four which is good because it means we can actually get a good result even with these strategic contributions <ref:2505.12010#pg3>.

Lu: They also discuss robustness against adversarial agents using Algorithm four for 2P-UPBReD, where they use a trimmed mean algorithm to handle the gradients <ref:2505.12010#pg4>.

Meng: Trimmed mean? So if there are some bad actors in the system, this method lets you discard the lowest and highest alpha fraction of gradient values during aggregation to keep things stable <ref:2505.12010#pg4>.

Jane: It sounds like they've put a lot of work into making it practical for real-world scenarios where you don't know exactly who the adversaries are <ref:2505.12010#pg4>.

Tom: And for the listener, this paper suggests that if we design the learning process correctly, we can move beyond just hoping people contribute enough data to actually enforcing it through a structured mechanism <ref:2505.12010#pg3>.

Lu: The implication is that by combining parameter learning with careful payment rules and a two-phase approach, we can find the sweet spot between achieving full contribution and staying budget balanced <ref:2505.12010#pg2>.

Meng: I wonder how much overhead this adds to the training process compared to just using standard FedAvg? They are trying to balance contribution and truthfulness while keeping things efficient <ref:2505.12010#pg3>.

Jane: That’s a question for the engineers, Meng. It seems like they’re trading some efficiency for a much better guarantee on participation and learning quality <ref:2505.12010#pg2>.

Tom: So, to recap, UPBReD lets them learn while contributing, T-UPBReD ensures truthfulness with payments, and 2P-UPBReD pushes for maximum contribution leading to w OPT <ref:2505.12010#pg3>.

Lu: Exactly. The two phases are key because the first phase sets up the dynamics, and the second phase uses monetary transfers to guarantee that agents contribute their maximum data <ref:2505.12010#pg3>.

Meng: And they show that this convergence to w OPT is budget balanced, which is a nice property because it means the system doesn't just get better models but also manages its resources well <ref:2505.12010#pg2>.

Jane: It moves the conversation from "will agents contribute?" to "how do we structure the system so they *must* contribute their maximum amount of data?" <ref:2505.12010#pg3>.

Title and authors: Tom: It’s a mechanism that makes rationality about contribution explicit, which is what was missing in earlier models <ref:2505.12010#pg3>.

Lu: And they show how this system converges to the pure strategy Nash equilibrium, which means it settles on a stable state where everyone's best response is to contribute their maximum data <ref:2505.12010#pg3>.

Meng: So for someone deploying this, it suggests that if you want high contribution and good model parameters, you need to build in these explicit incentives <ref:2505.12010#pg3>.

Jane: It’s interesting because they’ve managed to tie together parameter learning, incentive design, and achieving full data contribution all in one framework <ref:2505.12010#pg3>.

Tom: We need to remember that this whole paper is about how we can incentivize contribution and learn parameters too in federated learning with strategic data owners <ref:2505.12010#pg1>.

Lu: It’s a solid piece of work because it tackles the core problem of agent rationality in distributed learning <ref:2505.12010#pg3>.

Meng: I’m just thinking about how complex implementing all these payment rules is when you scale this to thousands of clients <ref:2505.12010#pg3>.

Jane: That’s a fair concern, Meng. The complexity of the T-UPBReD payment rule is something that needs careful engineering in practice <ref:2505.12010#pg3>.

Tom: So, for now, the big picture is that this paper shows a path to getting full data contribution and optimal parameter learning simultaneously by designing the right incentive structure <ref:2505.12010#pg3>.

Lu: And they provide convergence guarantees to show that this system actually works as intended in theory <ref:2505.12010#pg3>.

Jane: It’s a deep dive into how incentive design can fundamentally change the dynamics of federated learning systems <ref:2505.12010#pg3>.

Tom: Alright, that covers the main points for now as we wrap up on this paper about Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners <ref:2505.12010#pg1>.

Lu: It’s a very detailed approach to making sure agents are contributing in a way that benefits everyone <ref:2505.12010#pg3>.

Meng: I'll keep thinking about the practical scaling challenges of those monetary transfers, though <ref:2505.12010#pg3>.

Jane: We’ll leave you with this paper exploring how we can get full contribution without sacrificing the learning quality, and we’ll talk about some other interesting research next time.

The paper's summary: Tom: So, what we're looking at here is how they tackle that free-riding problem in federated learning by making agents actually contribute their data to help train the model.

Jane: Right, so basically, they've built a system with two main ideas—UPBReD and 2P-UPBReD—to make sure everyone contributes what they have while the central server learns the best model parameters <ref:2505.12010#pg1>.

Tom: UPBReD is their starting point, aiming for simultaneous learning and contribution, and they show it converges to a pure strategy Nash equilibrium.

Jane: But that's just the setup, right? The big part is 2P-UPBReD because it adds payment rules that incentivize agents to give their maximum data contribution <ref:2505.12010#pg1>.

Tom: Which means in the second phase, you get convergence to w OPT, which is the optimal model parameters, and this happens while everyone contributes their maximum amount of data.

Jane: And they also show this whole thing is robust against bad actors using a trimmed mean algorithm when there are small fractions of adversarial agents.

Tom: It’s about tying together parameter learning, incentive design through payments, and ensuring full contribution in one framework.

Jane: It changes the conversation from just hoping people contribute enough data to actually building a system that forces them to contribute their maximum amount of data for the best model possible.

Tom: Yeah, it shows that when you structure the incentives this way, you get a stable state where everyone's best move is to give their full contribution.

Jane: That leads us right into how they handle those tricky payment rules and what it actually means for someone using these models in the real world.

The paper's improvements: Tom: So, what's next is how they make this whole system even better than just adding some payment rules to the basic UPBReD framework.

Jane: Right, so they look at ways to refine those incentive structures and see if they can make the system more efficient or robust in practice.

Tom: They introduce something called FedAvgStrategic, which allows agents to choose a strategic contribution level based on what they've learned in previous rounds, and stick with that level.

Jane: That means the system isn't just reacting to the current state of the model; it learns an optimal contribution strategy and uses that same strategy all through training.

Tom: It’s about leveraging that learned optimal contribution across every round to potentially get faster convergence than if they were just following a static rule.

Jane: But they also have these ideas about budget constraints, which means they try to keep the total cost of running the system balanced while still encouraging full participation.

Tom: They are trying to find that sweet spot between getting the best model parameters and making sure it doesn't blow up on a client's resources.

Jane: It’s about balancing truthfulness, full data contribution, and keeping the overall budget in check across all participants.

Tom: The paper suggests this approach leads to better stability and potentially faster learning under those specific conditions where agents are contributing strategically.

Jane: So what does this mean for someone actually deploying a federated model? It means they have a more sophisticated way to handle client participation and resource management.

Tom: It moves beyond just having a basic mechanism for contribution; it’s about making the contribution itself intelligent and adaptive based on past performance.

Jane: And that brings us to how they handle the limitations of this framework, specifically what happens when you have a lot of different data types across clients.

Conclusion: Tom: So to wrap up, this paper, "Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners," shows how you can structure incentives to get agents to contribute their maximum data while the system learns the best model parameters.

Jane: Exactly, it’s a framework for tying together parameter learning, incentive design through payments, and ensuring full contribution in one system.

Tom: The two-phase approach with 2P-UPBReD guarantees convergence to w OPT, which is the optimal model parameters, and this happens while everyone contributes their maximum amount of data <ref:2505.12010#pg1>.

Jane: It really moves the conversation from just hoping people contribute enough data to actually building a system that forces them to contribute their maximum amount of data for the best model possible.

Tom: Yeah, it shows that when you structure the incentives this way, you get a stable state where everyone's best move is to give their full contribution.

Jane: That leads us right into how they handle those tricky payment rules and what it actually means for someone using these models in the real world.

Lu: From my side, I see this as a solid theoretical framework because it explicitly models the rationality of contribution which was missing from earlier methods.

Meng: But I still have to wonder about the overhead of setting up those payment rules; if you scale this to thousands of clients, that infrastructure gets heavy.

Lalam: Based on the literature, I'm very sure that this structure is sound for achieving full participation in these scenarios; it aligns perfectly with what we need for robust training.

Jane: So, the main thing here is getting a concrete answer on how to get maximum contribution without completely breaking the budget balance of the whole system.

Tom: It’s about building a mechanism that makes rationality about contribution explicit, which was missing in earlier models.

Lu: And they show how this converges to the pure strategy Nash equilibrium, which means it settles on a stable state where everyone's best response is to contribute their maximum data.

Meng: I just think we need real-world benchmarks before we can fully trust that convergence holds when you introduce these complex payment dynamics.

Lalam: I'm confident in the theoretical guarantees presented for this system, and it really pushes the boundary on what we can achieve in distributed learning.

Jane: So, to summarize, "Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners" gives us a path to full data contribution and optimal model learning through careful incentive design.

Tom: It’s a very detailed approach to making sure agents are contributing in a way that benefits everyone.

Lu: This is important because it shows that the structure of the protocol dictates the behavior of the agents, which is fundamental for building reliable AI systems.

Jane: We’ll talk more next time about how these kinds of incentive structures interact with other challenges we're facing in distributed learning.

Drashthi Doshi, Aditya Vema Reddy Kesari, Avishek Ghosh, Swaprava Nath, Suhas S Kowshik

Indian Institute of Technology Bombay · Amazon

cs.GT, cs.LG, cs.MA

Submitted: 2025-05-17

Updated: 2026-10-14

Comments: 52 pages, under review

License: http://creativecommons.org/licenses/by-nc-nd/4.0/

Importance score: 83/100

The gist: The gist: This paper addresses simultaneous parameter learning and incentivizing contribution in a truthful manner in federated learning through two mechanisms, UPBReD and 2P-UPBReD, which aim to

Key concepts

Free-riding: This occurs when some clients in federated learning exploit the system by contributing very little data and relying on others' contributions to learn a good model without putting in effort. This disincentivizes honest participants from sharing their data.
UPBReD: This is the first mechanism that allows clients to update both their contribution amount and the model parameters simultaneously. It converges to a Nash equilibrium where clients contribute optimally based on what they believe will be the best model for those contributions.
2P-UPBReD: This second mechanism addresses free-riding by adding budget-balanced transfers. This structure makes it a dominant strategy for clients to contribute all their data, ensuring the training converges to the welfare-maximizing model while maintaining budget balance.
T-2P-UPBReD: This mechanism adds payment rules to 2P-UPBReD to ensure that clients report their data contributions truthfully. It achieves this by comparing reported contributions against realized valuations of the trained model and pricing gradient reports with a budget-balanced charge.

Terminology

Federated Learning — 2× in this paper · explained in 17 episodes
Federated Learning (FL) — 1× in this paper · explained in 9 episodes
Nash Equilibrium — 1× in this paper · explained in 2 episodes

Summary

The gist: This paper addresses simultaneous parameter learning and incentivizing contribution in a truthful manner in federated learning through two mechanisms, UPBReD and 2P-UPBReD, which aim to achieve full data contribution with certain tradeoffs between budget balance and truthfulness

Motivation

Classical federated learning assumes that the clients have a limited amount of noisy data with which they voluntarily participate and contribute towards learning a global, more accurate model in a principled manner However, these methods do not consider the incentive of an agent for participating and contributing to the process, given that data collection and running a distributed algorithm is costly for the clients The question of rationality of contribution has been asked recently in the literature and some results exist that consider this problem In the classical federated learning problem, it is typically assumed that the agents participate with all of their data points voluntarily However, in the presence of rational users, such an assumption may not always hold since sampling data is costly for agents It may happen that some agents try to contribute very few data-points and try to exploit the system by learning based on others’ data-points. This phenomenon is called free-riding Karimireddy et al. (2022) and disincentivizes honest participants to contribute their data to the FL process

Proposed Mechanisms

The paper proposes two mechanisms to address the question of whether a mechanism can be designed that incentivises each user to participate and contribute their maximum data in FL and simultaneously learn the optimal model parameters The first mechanism is Updated Parameter Best Response Dynamics (UPBReD), which allows simultaneous learning and contribution by the agents in FL, and shows that it converges to a pure strategy Nash equilibrium (Theorem 1) The second mechanism is Two Phase Updated Parameter Best Response Dynamics (2P-UPBReD), which learns the optimal parameters for all agents and asks the data-consumers to pay and pays the data-contributors, without keeping any surplus

UPBReD Algorithm

Algorithm 1, called Updated Parameter Best Response Dynamics (UPBReD), allows simultaneous learning and contribution by the agents in FL, and shows that it converges to a pure strategy Nash equilibrium (Theorem 1) In every subsequent iteration t, each agent i locally computes two quantities: (i) its updated contribution s t+1 i by taking one gradient ascent step w.r.t. its own contribution si, and (ii) the local gradient of agent i’s valuation component of the social welfare w.r.t. w The center calculates an updated w t+1 that averages (in spirit of the FedAvg algorithm (McMahan et al., 2017)) all the local gradients sent by the agents

Truthful Elicitation with Payment Rules

To ensure truthful reporting, T-UPBReD adds payments to UPBReD to construct a mechanism T-UPBReD that elicits truthful reports from agents in every round (Theorem 2) The payment rule p star i(ˆθ t, vˆ t) is defined as p star i(ˆθ t, vˆ t) = ∑j̸=i vj(a t w(ˆθ t),ŝ t) − ϕ(v̂ t i, vi(a t w(θ t),ŝ t)) − hi(θ t−i) With this mechanism, we can show that T-UPBReD is EPIC in every round (Theorem 2)

Convergence to Optimal NE

In the second phase of Algorithm 2, where all agents contribute the maximum amount of data they own, it converges to w OPT Theorem 3 states that under Assumptions 3 and 4, for every w0 ∈ Rm and s0 ∈ ∏i∈N Si, 2P-UPBReD (Algorithm 2) converges to the Nash equilibrium (w OPT,s max) and is budget balanced This convergence is guaranteed by showing that the utility of every agent can be made strictly increasing in their own contribution si in the following manner

Robustness and Practicality

The paper demonstrates robustness to adversarial agents by describing a trimmed mean algorithm for 2P-UPBReD (Algorithm 4) In the second phase, instead of aggregating all gradients, for each coordinate of the gradients the values of the gradient corresponding to that coordinate are sorted, and the lowest and highest α fraction of these values are discarded Experiments show that 2P-UPBReD with trimmed mean is robust to adversaries when the fraction of adversarial agents is small The results in Sections 3 and 4 guarantee convergence of Algorithms 1 and 2 to a pure Nash equilibrium under the given assumptions

Conclusion

The paper proposes UPBReD, which ensures convergence to a Nash equilibrium with model parameters w, and its modification T-UPBReD, which enforces truthful reporting Since w may deviate from the optimal w OPT, we introduced a two-phase mechanism, 2P-UPBReD, that guarantees full agent participation and convergence to w OPT Future directions include incorporating agent budget constraints to preserve approximate guarantees and extending the framework to heterogeneous data settings

How it works

The paper addresses the question of simultaneous parameter learning and incentivizing contribution in a truthful manner in federated learning through two mechanisms, UPBReD and 2P-UPBReD, which aim to achieve full data contribution with certain tradeoffs between budget balance and truthfulness

UPBReD Algorithm

Improvements for AI systems

Bold header: Nash Equilibrium Achievement via 2P-UPBReD

This mechanism guarantees full agent participation and convergence to wOPT, at the cost of monetary transfers that can be internal among agents, allowing agents to contribute their maximum data while the center learns the optimal parameter.

Bold header: Truthful Reporting with T-UPBReD

The paper introduces T-UPBReD, which is designed to elicit truthful reports from agents in every round by employing a payment rule that penalizes utility gains from misreporting, ensuring an agent's utility remains bounded by the truthful outcome.

Bold header: Robustness Against Adversarial Agents via Trimmed Mean 2P-UPBReD

The system can be made robust to adversarial agents when the fraction of adversaries is small by implementing Algorithm 4 (Trimmed mean for 2P-UPBReD), which discards the lowest and highest gradient values during aggregation.

Bold header: Optimal Contribution Convergence

The two-phase algorithm, 2P-UPBReD, ensures that the agents to contribute smax i and the center learns the optimal parameter wOPT, achieving the desired outcome where smax is a fixed point of the update s t+1 = s t + γg(w0,s t, µ t) at the end of phase 1.

Bold header: Efficient Model Training under Strategic Contribution

FedAvgStrategic allows agents to choose to contribute their data strategically as in UPBReD, and stick to this contribution in the entire training phase, enabling faster convergence by leveraging the learned optimal contribution strategy across all rounds.

Abstract

In federated learning (FL), a center trains a shared model from updates that clients compute on their private data. Collecting and training on data is costly, so a strategic client may contribute little data and free-ride on the model trained by the others. We study mechanisms that incentivize data contribution and learn the model at the same time. Our first mechanism, UPBReD, updates the clients ' contributions and the model jointly, and converges at a geometric rate to a Nash equilibrium of the contribution game together with the welfare-maximizing model for those contributions. This equilibrium can involve under-contribution, so our second mechanism, 2P-UPBReD, adds budget-balanced transfers that make full contribution a dominant strategy, after which training converges to the optimal model; we also give conditions under which every client prefers to participate. Both mechanisms trust what clients report about the data they use and the gradients they compute. We show that no payment rule can reward unverifiable contributions unless something the center observes depends on the data actually used. Building on this, T-2P-UPBReD checks each client's reported contribution against the other clients ' realized valuations of the trained model, and prices gradient reports with a budget-balanced charge. It elicits truthful contribution reports, makes gradient reports approximately truthful, still reaches full contribution and the optimal model, and never runs a deficit. Experiments on CIFAR-10, FEMNIST, and Twitter show that 2P-UPBReD matches the welfare of FedAvg trained on all data, while strategic clients without incentives lower the welfare of the learned model.