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REVIEW 6 major objections 4 minor 36 references

DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information

T0 review · 6 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read DaringFed shows that an online federated learning server can set near-optimal rewards without knowing client computation resources or revealing its own bandwidth, by combining Bayesian persuasion signals with bandit-style dynamic pricing.

desk verdict A plausible pairing of Bayesian persuasion and UCB pricing for OFL, but the central 2ξ bound and the uniqueness claim are unsupported; the paper needs a serious technical referee. read the letter →

arxiv 2505.05842 v1 pith:N4EOTR2M submitted 2025-05-09 cs.GT cs.LGstat.ML

Yun Xin , Jianfeng Lu , Shuqin Cao , Gang Li , Haozhao Wang , Guanghui Wen This is my paper · ORCID
classification cs.GTcs.LGstat.ML
keywords Bayesianpersuasiononlinefederatedlearningincentivemechanismtwo-sidedincompleteinformationdynamicpricingupperconfidenceboundNashequilibriumsignaldesign
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to show that a server in online federated learning can set rewards for arriving clients without knowing those clients' computation power, and without clients knowing the bandwidth the server allocates. It models the exchange as a Bayesian persuasion game in which the server sends signals about bandwidth and sets a price, and it claims this game has a unique Bayesian persuasion Nash equilibrium. For the one-sided version the paper derives an explicit optimal signaling rule, and for the two-sided version it shows that a discretized, learnable version of the mechanism stays within a fixed additive gap of the optimal server cost. The paper reports that on four real image datasets the mechanism improves model accuracy and convergence speed, and synthetic tests show the learned reward and computation threshold converge.

What carries the argument

The central object is the DaringFed mechanism, a 2-tuple $(S,P)$: a Bayesian persuasion signal rule $S$ that maps a communication-resource value $\tau$ to a distribution over posterior means $\mu$, and a dynamic pricing rule $P$ that sets the reward $\gamma\leftarrow \rho(\sigma|\tau)\theta$. The signal distribution must satisfy three constraints (Bayesian consistency, Bayesian plausibility, and Bayesian benefit), and the pricing side estimates the client survival function $s(\theta)$ with an upper-confidence-bound estimate whose confidence term is $\sqrt{\ln N/(2N_t(\hat{\theta}))}$. Algorithm 1 searches the discretized reward space and resource-threshold space, uses Theorem 2's four-signal formulas to build the signal distribution, and Theorem 3's bound $c_s(\gamma^+,\rho^+)-c_s(\gamma^*,\rho^*)\le 2\xi$ certifies the gap between the approximate and optimal server cost. The optimal signal rule itself is the ratio formula $\rho(\mu|\tau)=\rho(\mu)(\tau-\mu)/(\lambda(\tau)(\tau-\underline{\tau}))$ and its mirror at the upper endpoint, which is what lets the server influence client beliefs without revealing the actual bandwidth.

What would settle it

Choose a prior distribution over communication resources that has substantial mass strictly inside the interval, compute the server's cost under the signaling rule of Theorem 1, and compare it with the true minimum over all feasible signal distributions obtained by brute-force search or convex optimization; if the two differ beyond the discretization gap, the theorem's optimality claim for general priors is false.

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Extended reading notes

Core claim

The central claim is that DaringFed, a two-part mechanism built from a Bayesian persuasion signal rule and a dynamic pricing rule, approximately solves the server's cost-minimization problem in online federated learning under two-sided incomplete information. The signal rule lets the server choose a posterior distribution over communication resources for each arriving client, subject to Bayesian consistency, plausibility, and benefit constraints, so that clients who lack bandwidth information update their beliefs in a way favorable to the server. The pricing rule estimates the unknown client computation-resource distribution via an upper-confidence-bound estimate of the survival function and then picks the reward and threshold on a discrete grid. The paper proves a unique Bayesian persuasion Nash equilibrium exists, gives the optimal signal formula for the one-sided case, and proves that the approximate solution found over the grid differs from the true optimum by at most twice the grid step. Empirically, the mechanism filters out low-resource clients and improves accuracy and convergence speed on four datasets, with the estimated reward and computation threshold converging in simulations.

Load-bearing premise

The proof of the optimal signal drops all interior values of the communication-resource distribution and uses only its two endpoints, so the claimed general formula for the optimal signal depends on an unstated assumption that the prior has no mass in between.

Editorial extensions

If this is right

  • The server can set rewards online without knowing the client computation-resource distribution; the upper-confidence-bound estimator supplies the missing information and the total loss is at most $2\xi$.
  • Clients are only shown a posterior signal about bandwidth and a reward, so the server's actual communication-resource allocation stays private.
  • Because the game has a unique Bayesian persuasion Nash equilibrium, neither side can improve its own payoff by unilateral changes to signal, reward, or participation rule.
  • The $\xi$ discretization directly trades optimality against computation: a smaller grid step gives closer-to-optimal cost at a larger search cost.
  • Filtering out low-resource clients through the reward threshold is what improves model accuracy and convergence speed in the real-data experiments, with a reported 16.99% improvement.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, a natural fix for the endpoint-only gap is to discretize the prior or to use the general convex-hull signal characterization from Bayesian persuasion; that would extend Theorem 1 to arbitrary priors.
  • Beyond the paper, replacing the Hoeffding-style confidence term with a variance-aware bandit bound could shrink the practical gap below $2\xi$ or reduce the number of rounds needed to converge, which the paper does not test.
  • Beyond the paper, the reported 16.99% accuracy gain depends on the four chosen datasets and the fixed cost model; stress tests with heavy-tailed resource distributions or non-stationary client populations would show whether the $2\xi$ bound remains the limiting factor.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

6 major / 4 minor

Summary. This paper studies incentive design for online federated learning under two-sided incomplete information: arriving clients know only a signal about the server's communication resource, and the server does not know the clients' computation-resource distribution. The proposed DaringFed mechanism combines a Bayesian-persuasion signal rule with a dynamic pricing rule; the server estimates the survival function of client computation resources by a UCB procedure and searches a discretized reward and signal space. The theoretical claims are a unique Bayesian-persuasion Nash equilibrium (Lemma 1), an optimal signal and reward for one-sided incomplete information (Theorem 1), an approximate signal for the two-sided case (Theorem 2), and a 2ξ gap between the approximate and optimal server cost (Theorem 3). Experiments on MNIST, Fashion-MNIST, FEMNIST, and CIFAR-10 compare DaringFed with three ablations, and synthetic experiments study convergence of the threshold and reward.

Significance. The modeling choice is relevant: both sides having private, dynamically changing resource information is a real obstacle for federated-learning incentives, and framing the interaction as Bayesian persuasion with a bandit-based estimation of the client-type distribution is a plausible design. The paper also provides a UCB estimator and an algorithmic instantiation in Algorithm 1. However, the core theoretical contributions are not currently supported: the equilibrium definition has reversed inequalities for a cost-minimizing server, the proof of uniqueness does not establish uniqueness, the optimal-signal derivation implicitly assumes a two-point prior, and the proof of the 2ξ bound contains a direction error that prevents the inequality chain from closing. Because these issues are load-bearing for the claimed approximate-optimality guarantee, the significance of the contribution cannot be assessed until the theory is substantially repaired.

major comments (6)
  1. [Section 4.2, Definition 5 (Eq. (14))] The inequalities in the BPNE definition are reversed relative to the paper's own objective. Equation (4) and Eq. (5) define the server's problem as minimizing cs, so a Nash equilibrium for the server should satisfy cs(γ*,ρ*) ≤ cs(γ*,ρ) and cs(γ*,ρ*) ≤ cs(γ,ρ*) for unilateral deviations. As written, Definition 5 requires cs(γ*,ρ*) ≥ cs(γ*,ρ) and cs(γ*,ρ*) ≥ cs(γ,ρ*), which would make the equilibrium the server's worst unilateral outcome rather than a best response. This sign error undermines the meaning of Lemma 1 and the subsequent equilibrium analysis.
  2. [Appendix, Proof of Lemma 1] The proof argues that, for fixed γ, a concavification argument gives an optimal signal, and that for fixed ρ monotonicity and continuity give an optimal reward. This establishes existence of optimal responses separately, not uniqueness of a joint equilibrium (γ*,ρ*) under the two unilateral-deviation conditions in Definition 5. No argument shows that the optimal signal is unique or that the intersection of the two best-response sets is a singleton. The claim that there exists a unique BPNE is therefore unsupported by the proof.
  3. [Appendix, Proof of Theorem 1 (Eqs. (23)-(24))] The derivation of the optimal signal ρ(μ|τ) uses only the prior masses λ(τ) and λ(τ̄) at the two endpoints of [τ,τ̄]; Eq. (23) is exactly the Bayesian-consistency equation for a prior supported on {τ,τ̄}. The theorem, however, is stated for a general prior over τ ∈ [τ,τ̄], and no two-point-support assumption is given in Section 5.1. If the prior has interior mass, the displayed formula in Eq. (15) does not follow and the claimed optimal signal rule is not established. This is load-bearing because Theorem 1 is the basis for the one-sided optimal design and for the structure of the approximate signals in Theorem 2.
  4. [Appendix, Proof of Theorem 3 (Eq. (30))] The inequality chain has a direction error. The proof states that a higher reward leads to a lower computation-resource threshold and concludes s(γ*,μ) ≥ s(γ+,μ). For γ+ ≥ γ*, the threshold θhat(γ+) = min{θ : c(θ,μ) ≤ γ+} is no larger than θhat(γ*), and because s is non-increasing in θ, the correct inequality is s(γ+,μ) ≥ s(γ*,μ). With the correct direction, the term γ*[∫∫ρ+s(γ+,μ)dμdτ − ∫∫ρ*s(γ*,μ)dμdτ] in the final step of Eq. (30) is nonnegative unless a relation between ρ+ and ρ* makes it negative; no such relation, and no Lipschitz or continuity bound on s in γ, is provided. The chain therefore cannot be closed, and the claimed bound cs(γ+,ρ+) − cs(γ*,ρ*) ≤ 2ξ is unproven.
  5. [Appendix, Proofs of Theorems 2 and 3] Both proofs begin by assuming γ*+ξ ≤ γ+ ≤ γ*+2ξ for the discrete reward γ+ chosen from a grid with spacing ξ. This condition is generally false: for any real γ*, a grid of spacing ξ contains a grid point within ξ of γ*, and if γ* is itself a grid point the nearest point is γ+. The asserted lower bound γ*+ξ is not guaranteed unless one defines γ+ as the first grid point strictly above γ*+ξ, whose existence is not established for a finite grid. Consequently, the 2ξ bound is not a consequence of the discretization in the way the proof claims, and the central approximate-optimality guarantee is unsupported.
  6. [Section 3.3 and Definition 4] The stated objective is to minimize the expected server cost cs = γ·Pr(participation), as in Eq. (4) and Eq. (13), but BayesBen in Eq. (11) requires the signaling rule to satisfy E_ρ[ψ] ≥ E_λ[ψ], i.e., signaling cannot reduce the participation probability. For a cost-minimizing server, higher participation probability at a fixed positive reward is costly, so this constraint is not aligned with the stated objective. If participation is beneficial only through reaching the accuracy target, that benefit must be modeled explicitly; as written, the formulation is internally inconsistent.
minor comments (4)
  1. [Section 4.2, Definition 1, Eq. (6)] The definition of the signal rule uses an assignment arrow '←' instead of a functional equality; the expression ρ(σ|τ) ← φ(τ|σ)/λ(τ) should be written as an equality, and the densities should be defined on the same measurable space.
  2. [Appendix, Proof of Theorem 1, Eq. (25)] The proof uses p(γ,μ) in the server cost expression, while the theorem statement and the rest of Section 5 use s(γ,μ); either p is a typo for s or it must be defined.
  3. [Lemma 2 and Algorithm 1] Equation (16) divides by N_t(θhat), which can be zero for an unselected threshold; the algorithm and the UCB bound need an explicit initialization or pseudo-count treatment.
  4. [Section 6, Table 1 and Figure 2] The empirical section reports no error bars or multiple-seed statistics, and Table 1 gives final accuracy only; the claimed 16.99% improvement in accuracy and convergence speed is not broken down, and all baselines are ablations of DaringFed rather than existing incentive mechanisms.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the derivation is self-contained; proof-quality issues in Theorems 1 and 3 are correctness gaps, not self-referential reductions.

full rationale

No load-bearing step in DaringFed's derivation chain reduces to its own inputs. The mechanism is defined as a tuple (S,P) with Bayesian-consistency, Bayesian-plausibility, and Bayesian-benefit constraints, and Lemma 1 and Theorem 1 are argued from the concave survival function and Bayes plausibility rather than from the theorem conclusions. The UCB survival estimate in Lemma 2 is a standard data-driven estimator built from observed participation feedback, and Algorithm 1 optimizes over the estimated objective; the approximate solution is not a fitted parameter renamed as a prediction. Theorem 3's 2ξ gap is presented as a derived discretization bound, not as a quantity defined to equal the grid spacing; even though the proof contains a sign error in the monotonicity comparison and drops the participation-probability term, an invalid proof is a correctness gap, not a circular reduction, because the paper does not define the bound in terms of itself. The Theorem 1 derivation implicitly uses only the two prior endpoints τ and τ̄, which is an unstated-support assumption rather than a self-referential construction. The only author-overlap citation (Lu et al. 2023) supports a standard convexity assumption and is not load-bearing. Therefore the honest finding is no significant circularity, score 0.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central results rest on two standard convexity/concavity assumptions plus two unstated reductions: a two-point prior on communication resources in Theorem 1, and the client deciding based only on the posterior mean of τ. The discretization parameter ξ is a free design knob that sets the claimed optimality gap.

free parameters (4)
  • ξ = 0.01 in synthetic experiments
    Discretization step for reward and computation resource spaces; controls the claimed optimality gap 2ξ and is chosen by the designer.
  • β
    Design threshold ensuring clients with computation resources below β are filtered; appears in the cost minimization constraint.
  • α
    Weighting factor in the global model update (Eq. 2); not specified in the paper.
  • η
    Learning rate in the local update (Eq. 1); not specified in the paper.
assumptions (5)
  • domain assumption Assumption 1: Client cost function c(θ,τ) is non-increasing and convex in both θ and τ.
    Used in Lemma 1 to show the threshold θ̂ is convex in µ and to justify existence of optimal reward; standard but not universal.
  • domain assumption Assumption 2: Survival function s(θ) is non-increasing and concave in θ.
    Used to claim s(γ,µ) is concave, which drives the persuasion benefit; chosen in synthetic experiments as s(θ)=1-((θ-0.1)/0.8)^8.
  • domain assumption θ and τ are i.i.d. across clients, allowing the time subscript to be dropped in Section 4.1.
    The paper states this assumption before Eq. (5); it simplifies the problem but may not hold in dynamic real-world settings.
  • ad hoc to paper The prior distribution of communication resources has two-point support at τ and τ̄ in Theorem 1.
    The proof of Theorem 1 uses only λ(τ) and λ(τ̄) and drops interior τ; this is an unstated assumption not present in the theorem statement.
  • ad hoc to paper Client participation depends only on the posterior mean µ of communication resources, i.e., cost is evaluated at c(θ,µ).
    The reformulation in Eq. (8) replaces τ by µ in the cost function, assuming the client's decision is linear in the posterior expectation.

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Cite this review

Pith. "Pith review of DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information." pith.science (2026). https://pith.science/paper/N4EOTR2M

@misc{pith2026250505842,
  author       = {Pith},
  title        = {Pith review of: DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/N4EOTR2M}},
  note         = {Machine review of arXiv:2505.05842}
}
read the original abstract

Online Federated Learning (OFL) is a real-time learning paradigm that sequentially executes parameter aggregation immediately for each random arriving client. To motivate clients to participate in OFL, it is crucial to offer appropriate incentives to offset the training resource consumption. However, the design of incentive mechanisms in OFL is constrained by the dynamic variability of Two-sided Incomplete Information (TII) concerning resources, where the server is unaware of the clients' dynamically changing computational resources, while clients lack knowledge of the real-time communication resources allocated by the server. To incentivize clients to participate in training by offering dynamic rewards to each arriving client, we design a novel Dynamic Bayesian persuasion pricing for online Federated learning (DaringFed) under TII. Specifically, we begin by formulating the interaction between the server and clients as a dynamic signaling and pricing allocation problem within a Bayesian persuasion game, and then demonstrate the existence of a unique Bayesian persuasion Nash equilibrium. By deriving the optimal design of DaringFed under one-sided incomplete information, we further analyze the approximate optimal design of DaringFed with a specific bound under TII. Finally, extensive evaluation conducted on real datasets demonstrate that DaringFed optimizes accuracy and converges speed by 16.99%, while experiments with synthetic datasets validate the convergence of estimate unknown values and the effectiveness of DaringFed in improving the server's utility by up to 12.6%.

Figures

Figures reproduced from arXiv: 2505.05842 by the authors.

Figure 1
Figure 1. An overview of OFL. If the client decides to engage in FL training, the server sends the latest global model parameters ωt to the client, and the client sends back the updated local model parameters ω n t+1 to the server after finishing local training. Otherwise, the server needs to wait for the next client arrive and participate in train￾ing (step ⃝3 ). Finally, the server updates the global model ωt+1 according to… view at source ↗
Figure 2
Figure 2. Testing the accuracy of OFL with the proposed DaringFed on (a) MNIST, (b) Fashion-MNIST, (c) FEMNIST, and (d) CIFAR-10. [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Convergence on (a) ˆθ and (b) γ. Datasets Accuracy (%) DF DF-B DF-D DF-BD MNIST 88.38 87.56 86.93 83.35 Fashion-MNIST 69.11 68.65 68.14 67.18 FEMNIST 64.41 57.41 60.35 43.05 CIFAR-10 34.73 34.27 34.48 31.73 [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

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Reference graph

Works this paper leans on

36 extracted references · 33 canonical work pages

  1. [1]

    Communication-Efficient Learning of Deep Networks from Decentralized Data.Artificial Intelligence and Statistics, 54:273–1282,

    [McMahanet al., 2017 ] Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Arcas. Communication-Efficient Learning of Deep Networks from Decentralized Data.Artificial Intelligence and Statistics, 54:273–1282,

    work page 2017

  2. [4]

    The Data Value Based Asynchronous Federated Learning for UA V Swarm Under Unstable Com- munication Scenarios.IEEE Transactions on Mobile Com- puting, 23(6):7165-7179,

    [Cuiet al., 2023 ] Zhenhua Cui, Tao Yang, Xiaofeng Wu, Hui Feng, and Bo Hu. The Data Value Based Asynchronous Federated Learning for UA V Swarm Under Unstable Com- munication Scenarios.IEEE Transactions on Mobile Com- puting, 23(6):7165-7179,

    work page 2023

  3. [8]

    Dynamic Pricing and Learning with Bayesian Persuasion.Proceedings of the Advances in Neural In- formation Processing Systems, pages 1-9, New Orleans, USA, December

    [Agrawalet al., 2024 ] Shipra Agrawal, Yiding Feng, and Wei Tang. Dynamic Pricing and Learning with Bayesian Persuasion.Proceedings of the Advances in Neural In- formation Processing Systems, pages 1-9, New Orleans, USA, December

    work page 2024

  4. [10]

    Enhancing Federated Learning With Spectrum Allocation Optimiza- tion and Device Selection.IEEE/ACM Transactions on Networking, 31(5):1981-1996,

    [Zhanget al., 2023 ] Tinghao Zhang, Kwok Lam, Jun Zhao, Feng Li, Huimei Han, and Norziana Jamil. Enhancing Federated Learning With Spectrum Allocation Optimiza- tion and Device Selection.IEEE/ACM Transactions on Networking, 31(5):1981-1996,

    work page 2023

  5. [11]

    [Sahaet al., 2022 ] Rituparna Saha, Sudip Misra, Aishwariya Chakraborty, Chandranath Chatterjee, and Pallav K. Deb. Data-Centric Client Selection for Federated Learning over Distributed Edge Networks.IEEE Transactions on Paral- lel and Distributed Systems, 34(2):675-686,

    work page 2022

  6. [12]

    Incentivizing Federated Learning under Long- Term Energy Constraint via Online Randomized Auc- tions.IEEE Transactions on Wireless Communications, 21(7):5129 - 5144,

    [Yuanet al., 2021 ] Yulan Yuan, Lei Jiao, Konglin Zhu, and Lin Zhang. Incentivizing Federated Learning under Long- Term Energy Constraint via Online Randomized Auc- tions.IEEE Transactions on Wireless Communications, 21(7):5129 - 5144,

    work page 2021

  7. [14]

    Chen, Peng Yang, and Yuezhi Zhou

    [Denget al., 2022 ] Yongheng Deng, Feng Lyu, Ju Ren, Yi C. Chen, Peng Yang, and Yuezhi Zhou. Improving Federated Learning With Quality-Aware User Incentive and Auto- Weighted Model Aggregation.IEEE Transactions on Par- allel and Distributed Systems,33(12):4515-4529,

    work page 2022

  8. [15]

    LoMar: A Local De- fense Against Poisoning Attack on Federated Learning

    [Liet al., 2021 ] Xingyu Li, Zhe Qu, Shangqing Zhao, Bo Tang, Zhuo Lu, and Yao Liu. LoMar: A Local De- fense Against Poisoning Attack on Federated Learning. IEEE Transactions on Dependable and Secure Computing, 20(1):437-450,

    work page 2021

Show all 36 references
  1. [16]

    A Fair Classifier Using Kernel Density Es- timation.Proceedings of the Advances in Neural Infor- mation Processing Systems, pages 1-12, December

    [Choet al., 2020 ] Jaewoong Cho, Gyeongjo Hwang, and Changho Suh. A Fair Classifier Using Kernel Density Es- timation.Proceedings of the Advances in Neural Infor- mation Processing Systems, pages 1-12, December

    2020

  2. [17]

    [Zhaoet al., 2023 ] Yuxi Zhao, Xiaowen Gong, and Shiwen Mao

    MIT Press. [Zhaoet al., 2023 ] Yuxi Zhao, Xiaowen Gong, and Shiwen Mao. Truthful Incentive Mechanism for Federated Learn- ing with Crowdsourced Data Labeling.Proceedings of the IEEE Conference on Computer Communications, pages 1- 10, Rome, Italy, May

    2023

  3. [18]

    [Huanget al., 2022 ] Guangjing Huang, Xu Chen, Tao Ouyang, Qian Ma, Lin Chen, and Junshan Zhang

    IEEE. [Huanget al., 2022 ] Guangjing Huang, Xu Chen, Tao Ouyang, Qian Ma, Lin Chen, and Junshan Zhang. Col- laboration in Participant-Centric Federated Learning: A Game-Theoretical Perspective.IEEE Transactions on Mo- bile Computing, 22(11):6311-6326

    2022

  4. [19]

    Bayesian Persuasion.American Economic Review, 101(6):2590–2615,

    [Kamenica and Gentzkow, 2011] Emir Kamenica and Matthew Gentzkow. Bayesian Persuasion.American Economic Review, 101(6):2590–2615,

    2011

  5. [21]

    [Murhekaret al., 2024 ] Aniket Murhekar, Zhuowen Yuan, Bhaskar Ray Chaudhury, Bo Li, and Ruta Mehta. Incen- tives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization.Proceedings of the Advances in Neural Information Processing Systems, pages 1-...

    2024

  6. [22]

    [Bergstromet al., 1986 ] Theodore Bergstrom, Lawrence Blume, and Hal Varian

    MIT Press. [Bergstromet al., 1986 ] Theodore Bergstrom, Lawrence Blume, and Hal Varian. On the private provision of public goods.Journal of Public Economics, 29(1):25-49,

    1986

  7. [25]

    Fashion-mnist: a novel image dataset for bench- marking machine learning algorithms.arXiv preprint arXiv:1708.07747,

    [Xiaoet al., 2017 ] Han Xiao, Kashif Rasul, and Roland V ollgraf. Fashion-mnist: a novel image dataset for bench- marking machine learning algorithms.arXiv preprint arXiv:1708.07747,

    2017 arXiv

  8. [26]

    LEAF: A benchmark for federated settings.arXiv preprint arXiv:1812.01097,

    [Caldaset al., 2018 ] Sebastian Caldas, Sai Duddu, Peter Wu, Tian Li, Jakub Konec, Brendan McMahan, Virginia Smith, and Ameet Talwalkar1. LEAF: A benchmark for federated settings.arXiv preprint arXiv:1812.01097,

    2018 arXiv

  9. [29]

    Joint Participation Incentive and Network Pric- ing Design for Federated Learning.Proceedings of the IEEE Conference on Computer Communications, pages 1- 10, Rome, Italy, May

    [Dinget al., 2023 ] Ningning Ding, Lin Gao, and Jianwei Huang. Joint Participation Incentive and Network Pric- ing Design for Federated Learning.Proceedings of the IEEE Conference on Computer Communications, pages 1- 10, Rome, Italy, May

    2023

  10. [30]

    [Huet al., 2022 ] Miao Hu, Di Wu, Yipeng Zhou, Xu Chen, and Min Chen

    IEEE. [Huet al., 2022 ] Miao Hu, Di Wu, Yipeng Zhou, Xu Chen, and Min Chen. Incentive-Aware Autonomous Client Par- ticipation in Federated Learning.IEEE Transactions on Parallel and Distributed Systems, 33(10):2612-2627,

    2022

  11. [31]

    Incentive Mechanism De- sign for Joint Resource Allocation in Blockchain-based Federated Learning.IEEE Transactions on Parallel and Distributed Systems, 34(5):1536-1547,

    [Wanget al., 2023 ] Zhilin Wang, Qin Hu, Ruinian Li, Minghui Xu, and Zehui Xiong. Incentive Mechanism De- sign for Joint Resource Allocation in Blockchain-based Federated Learning.IEEE Transactions on Parallel and Distributed Systems, 34(5):1536-1547,

    2023

  12. [32]

    AoI-aware Incen- tive Mechanism for Mobile Crowdsensing using Stack- elberg Game.Proceedings of the IEEE Conference on Computer Communications, pages 1-10, Rome, Italy, May

    [Mingjunet al., 2023 ] Mingjun Xiao, Yin Xu, Jinrui Zhou, Jie Wu, Sheng Zhang, and Jun Zheng. AoI-aware Incen- tive Mechanism for Mobile Crowdsensing using Stack- elberg Game.Proceedings of the IEEE Conference on Computer Communications, pages 1-10, Rome, Italy, May

    2023

  13. [33]

    [Mitraet al., 2021 ] Aritra Mitra, Hamed Hassani, and George J

    IEEE. [Mitraet al., 2021 ] Aritra Mitra, Hamed Hassani, and George J. Pappas Online Federated Learning.Proceedings of the IEEE Conference on Decision and Control, pages 4083-4090, Austin, Texas, December

    2021

  14. [34]

    [Panget al., 2023 ] Jinlong Pang, Jieling Yu, Ruiting Zhou, and John C.S

    IEEE. [Panget al., 2023 ] Jinlong Pang, Jieling Yu, Ruiting Zhou, and John C.S. Lui. An Incentive Auction for Heteroge- neous Client Selection in Federated Learning.IEEE Trans- actions on Mobile Computing, 22(10):5733-5750,

    2023

  15. [35]

    [Hanet al., 2020 ] Pengchao Han, Shiqiang Wang, and Kin K. Leung. Adaptive Gradient Sparsification for Effi- cient Federated Learning: An Online Learning Approach. Proceedings of the IEEE International Conference on Distributed Computing Systems, pages 1-11, Singapore, February

    2020

  16. [36]

    [Damaskinoset al., 2022 ] Georgios Damaskinos, Rachid Guerraoui, Anne-Marie Kermarrec, Vlad Nitu, Rhicheek Patra, and Francois Taiani

    IEEE. [Damaskinoset al., 2022 ] Georgios Damaskinos, Rachid Guerraoui, Anne-Marie Kermarrec, Vlad Nitu, Rhicheek Patra, and Francois Taiani. FLeet: Online Federated Learning via Staleness Awareness and Performance Pre- diction.ACM Transactions on Intelligent Systems and Techno...

    2022

  17. [1986]

    Convex Optimization

    [Boyd, 2004] Stephen Boyd. Convex Optimization

    2004

  18. [2004]

    MNIST Hand-written Digit Database

    [Yannet al., 2021 ] Yann LeCun, Corinna Cortes, and Christopher Burges. MNIST Hand-written Digit Database

    2021

  19. [2009]

    [Wanget al., 2022 ] Xiaofei Wang, Yunfeng Zhao, Chao Qiu, Zhicheng Liu, Jiangtian Nie, and Victor C. Leung. In- FEDge: A Blockchain-Based Incentive Mechanism in Hi- erarchical Federated Learning for End-Edge-Cloud Com- munications.IEEE Journal on Selected Areas in Commu- nicat...

    2022

  20. [2011]

    Incentiviz- ing Proportional Fairness for Multi-Task Allocation in Crowdsensing.IEEE Transactions on Services Comput- ing, 17(3):990-1000,

    [Luet al., 2023 ] Jianfeng Lu, Haibo Liu, Riheng Jia, Zhao Zhang, Xiong Wang, and Jiangtao Wang. Incentiviz- ing Proportional Fairness for Multi-Task Allocation in Crowdsensing.IEEE Transactions on Services Comput- ing, 17(3):990-1000,

    2023

  21. [2017]

    Online Client Selection for Asynchronous Federated Learning With Fairness Consideration.IEEE Transactions on Wireless Communications, 22(4):2493-2506,

    [Zhuet al., 2022 ] Hongbin Zhu, Yong Zhou, Hua Qian, Yuanming Shi, Xu Chen, and Yang Yang. Online Client Selection for Asynchronous Federated Learning With Fairness Consideration.IEEE Transactions on Wireless Communications, 22(4):2493-2506,

    2022

  22. [2018]

    Learning Multiple Layers of Features from Tiny Images.Technical Report,

    [Krizhevsky, 2009] Alex Krizhevsky. Learning Multiple Layers of Features from Tiny Images.Technical Report,

    2009

  23. [2019]

    Asynchronous Online Federated Learning for Edge Devices with Non-IID Data.Proceed- ings of the IEEE International Conference on Big Data, pages 15–24, Atlanta, USA, December

    [Chenet al., 2020 ] Yujing Chen, Yue Ning, Martin Slawski, and Huzefa Rangwala. Asynchronous Online Federated Learning for Edge Devices with Non-IID Data.Proceed- ings of the IEEE International Conference on Big Data, pages 15–24, Atlanta, USA, December

    2020

  24. [2020]

    [Donget al., 2023 ] Xuewen Dong, Zhichao You, Ximeng Liu, Yuanxiong Guo, Yulong Shen, and Yanmin Gong

    IEEE. [Donget al., 2023 ] Xuewen Dong, Zhichao You, Ximeng Liu, Yuanxiong Guo, Yulong Shen, and Yanmin Gong. Federated and Online Dynamic Spectrum Access for Mo- bile Secondary Users.IEEE Transactions on Wireless Communications, 23(1):621-636,

    2023

  25. [2021]

    Optimal Contract Design for Efficient Fed- erated Learning With Multi-Dimensional Private Informa- tion.IEEE Journal on Selected Areas in Communications, 39(1):186-200,

    [Dinget al., 2020 ] Ningning Ding, Zhixuan Fang, and Jian- wei Huang. Optimal Contract Design for Efficient Fed- erated Learning With Multi-Dimensional Private Informa- tion.IEEE Journal on Selected Areas in Communications, 39(1):186-200,

    2020

  26. [2022]

    Power of Redundancy: Surplus Client Scheduling for Federated Learning against User Uncertainties.IEEE Transactions on Mobile Com- puting, 22(9):5449-5462,

    [Liet al., 2022 ] Youqi Li, Fan Li, Lixing Chen, Liehuang Zhu, Pan Zhou, and Yu Wang. Power of Redundancy: Surplus Client Scheduling for Federated Learning against User Uncertainties.IEEE Transactions on Mobile Com- puting, 22(9):5449-5462,

    2022

  27. [2023]

    Asynchronous federated optimization.arXiv preprint arXiv:1903.03934,

    [Xieet al., 2019 ] Cong Xie, Sanmi Koyejo, and Indranil Gupta. Asynchronous federated optimization.arXiv preprint arXiv:1903.03934,

    2019 arXiv

  28. [2024]

    [Huet al., 2022 ] Miao Hu, Wenzhuo Yang, Zhenxiao Luo, Xuezheng Liu, Yipeng Zhou, and Xu Chen

    MIT Press. [Huet al., 2022 ] Miao Hu, Wenzhuo Yang, Zhenxiao Luo, Xuezheng Liu, Yipeng Zhou, and Xu Chen. AutoFL: A Bayesian Game Approach for Autonomous Client Partici- pation in Federated Edge Learning.IEEE Transactions on Mobile Computing, 23(1):194-208,

    2022

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.