On the public citation graph

Optimizing Privacy, Utility, and Efficiency in a Constrained Multi-Objective Federated Learning Framework

https://doi.org/10.1145/3701039
0 signed · 77 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/mcda.1605openalex-snapshot
Surrogate-assisted multicriteria optimization: Complexities, prospective solutions, and business case
inferredcites →https://doi.org/10.1007/3-540-45712-7openalex-snapshot
Parallel Problem Solving from Nature — PPSN VII
inferredcites →https://doi.org/10.1007/3-540-45712-7_29openalex-snapshot
Bayesian Optimization Algorithms for Multi-objective Optimization
inferredcites →https://doi.org/10.1007/978-3-030-63076-8_17openalex-snapshot
Federated Learning for Open Banking
inferredcites →https://doi.org/10.1007/978-3-031-02462-7_21openalex-snapshot
Optimising Communication Overhead in Federated Learning Using NSGA-II
inferredcites →https://doi.org/10.1007/978-3-540-30217-9_75openalex-snapshot
SPEA2+: Improving the Performance of the Strength Pareto Evolutionary Algorithm 2
inferredcites →https://doi.org/10.1007/978-3-540-88910-6openalex-snapshot
Sequential Approximate Multiobjective Optimization Using Computational Intelligence
inferredcites →https://doi.org/10.1007/978-981-97-2259-4_18openalex-snapshot
SecureBoost$$+$$: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Tree
inferredcites →https://doi.org/10.1007/bfb0029723openalex-snapshot
Parallel Problem Solving from Nature
inferredcites →https://doi.org/10.1007/s10479-021-04033-zopenalex-snapshot
The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning
inferredcites →https://doi.org/10.1007/s11831-022-09859-9openalex-snapshot
A Review on Constraint Handling Techniques for Population-based Algorithms: from single-objective to multi-objective optimization
inferredcites →https://doi.org/10.1007/s12065-022-00718-xopenalex-snapshot
Communication efficiency optimization in federated learning based on multi-objective evolutionary algorithm
inferredcites →https://doi.org/10.1016/j.asoc.2015.04.061openalex-snapshot
Distributed evolutionary algorithms and their models: A survey of the state-of-the-art
inferredcites →https://doi.org/10.1016/j.crma.2012.03.014openalex-snapshot
Multiple-gradient descent algorithm (MGDA) for multiobjective optimization
inferredcites →https://doi.org/10.1016/j.swevo.2018.10.007openalex-snapshot
Multi-Objective Bayesian Global Optimization using expected hypervolume improvement gradient
inferredcites →https://doi.org/10.1109/4235.996017openalex-snapshot
A fast and elitist multiobjective genetic algorithm: NSGA-II
inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot
Gradient-based learning applied to document recognition
inferredcites →https://doi.org/10.1109/cvpr46437.2021.01607openalex-snapshot
See through Gradients: Image Batch Recovery via GradInversion
inferredcites →https://doi.org/10.1109/itw.2018.8613451openalex-snapshot
Optimal Utility-Privacy Trade-off with Total Variation Distance as a Privacy Measure
inferredcites →https://doi.org/10.1109/jiot.2020.2985694openalex-snapshot
Hierarchical Incentive Mechanism Design for Federated Machine Learning in Mobile Networks
inferredcites →https://doi.org/10.1109/tbdata.2022.3188292openalex-snapshot
Privacy-Preserving Federated Adversarial Domain Adaptation Over Feature Groups for Interpretability
inferredcites →https://doi.org/10.1109/tcomm.2021.3083316openalex-snapshot
Fast Federated Learning by Balancing Communication Trade-Offs
inferredcites →https://doi.org/10.1109/tevc.2007.892759openalex-snapshot
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
inferredcites →https://doi.org/10.1109/tevc.2013.2281535openalex-snapshot
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
inferredcites →https://doi.org/10.1109/tevc.2019.2895108openalex-snapshot
Approximating Hypervolume and Hypervolume Contributions Using Polar Coordinate
inferredcites →https://doi.org/10.1109/tevc.2020.3047835openalex-snapshot
Paired Offspring Generation for Constrained Large-Scale Multiobjective Optimization
inferredcites →https://doi.org/10.1109/tevc.2021.3099448openalex-snapshot
Real-Time Federated Evolutionary Neural Architecture Search
inferredcites →https://doi.org/10.1109/tevc.2022.3144880openalex-snapshot
A Survey on Knee-Oriented Multiobjective Evolutionary Optimization
inferredcites →https://doi.org/10.1109/tifs.2019.2903658openalex-snapshot
Optimal Utility-Privacy Trade-Off With Total Variation Distance as a Privacy Measure
inferredcites →https://doi.org/10.1109/tifs.2020.2988575openalex-snapshot
Federated Learning With Differential Privacy: Algorithms and Performance Analysis
inferredcites →https://doi.org/10.1109/tnnls.2019.2919699openalex-snapshot
Multi-Objective Evolutionary Federated Learning
inferredcites →https://doi.org/10.1109/tnnls.2022.3216981openalex-snapshot
Privacy and Robustness in Federated Learning: Attacks and Defenses
inferredcites →https://doi.org/10.1109/tnse.2022.3169117openalex-snapshot
Federated Learning Meets Multi-Objective Optimization
inferredcites →https://doi.org/10.1115/1.4051787openalex-snapshot
A Multi-Objective Bayesian Optimization Approach Using the Weighted Tchebycheff Method
inferredcites →https://doi.org/10.1142/9781800610460_0010openalex-snapshot
Multi-objective Optimization
inferredcites →https://doi.org/10.1145/1273496openalex-snapshot
Proceedings of the 24th international conference on Machine learning
inferredcites →https://doi.org/10.1145/2976749.2978318openalex-snapshot
Deep Learning with Differential Privacy
inferredcites →https://doi.org/10.1145/3133956.3133982openalex-snapshot
Practical Secure Aggregation for Privacy-Preserving Machine Learning
inferredcites →https://doi.org/10.1145/3298981openalex-snapshot
Federated Machine Learning
inferredcites →https://doi.org/10.1145/3339474openalex-snapshot
Comparison and Modelling of Country-level Microblog User and Activity in Cyber-physical-social Systems Using Weibo and Twitter Data
inferredcites →https://doi.org/10.1145/3375627.3375840openalex-snapshot
A Fairness-aware Incentive Scheme for Federated Learning
inferredcites →https://doi.org/10.1145/3378679.3394533openalex-snapshot
LDP-Fed
inferredcites →https://doi.org/10.1145/3383313.3411528openalex-snapshot
A Federated Recommender System for Online Services
inferredcites →https://doi.org/10.1145/3501296openalex-snapshot
Federated Learning for Smart Healthcare: A Survey
inferredcites →https://doi.org/10.1145/3543873.3587681openalex-snapshot
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness and Privacy
inferredcites →https://doi.org/10.1145/3555803openalex-snapshot
Trustworthy AI: From Principles to Practices
inferredcites →https://doi.org/10.1145/3563219openalex-snapshot
No Free Lunch Theorem for Security and Utility in Federated Learning
inferredcites →https://doi.org/10.1145/359168.359176openalex-snapshot
How to share a secret
inferredcites →https://doi.org/10.1145/3595185openalex-snapshot
Trading Off Privacy, Utility, and Efficiency in Federated Learning
inferredcites →https://doi.org/10.1609/aaai.v36i9.21283openalex-snapshot
A First Mathematical Runtime Analysis of the Non-dominated Sorting Genetic Algorithm II (NSGA-II)
inferredcites →https://doi.org/10.24963/ijcai.2022/324openalex-snapshot
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning
inferredcites →https://doi.org/10.3390/jsan12010013openalex-snapshot
Building Trusted Federated Learning: Key Technologies and Challenges
inferredcites →https://doi.org/10.3390/sym14051070openalex-snapshot
Optimizing Multi-Objective Federated Learning on Non-IID Data with Improved NSGA-III and Hierarchical Clustering
inferredcites →https://doi.org/10.3929/ethz-a-004284029openalex-snapshot
SPEA2: Improving the strength pareto evolutionary algorithm
inferredcites →https://doi.org/10.48550/arxiv.1305.6000openalex-snapshot
Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation
inferredcites →https://doi.org/10.52202/068431-1398openalex-snapshot
Pareto Set Learning for Expensive Multi-Objective Optimization

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-032-01940-0_3openalex-snapshot
Federated Learning for Enterprise AI
inferred← citeshttps://doi.org/10.1007/978-3-032-28924-7_35openalex-snapshot
PRIME-Guard: Metric-Constrained Joint Optimization of Robustness and Privacy with Bounded Accuracy Drift
inferred← citeshttps://doi.org/10.1007/s10462-026-11555-0openalex-snapshot
Survey of privacy preserving federated tree-based learning
inferred← citeshttps://doi.org/10.1016/b978-0-44-344433-3.00017-4openalex-snapshot
Robust defense against inference attacks and differential privacy integration in federated learning
inferred← citeshttps://doi.org/10.1016/j.cosrev.2026.100932openalex-snapshot
Blockchain-enabled defenses in federated learning: A comprehensive survey
inferred← citeshttps://doi.org/10.1016/j.ejor.2026.06.033openalex-snapshot
Pareto fronts for privacy-utility trade-offs in multi-query systems
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.129882openalex-snapshot
AdaptiveStreamFL: A Bayesian-enhanced multi-scale federated learning framework for dynamic data streams with uncertainty quantification
inferred← citeshttps://doi.org/10.1016/j.inffus.2025.103657openalex-snapshot
Distributed machine learning for next-generation communication networks: A survey on privacy, fairness, efficiency, and trade-offs
inferred← citeshttps://doi.org/10.1016/j.iot.2025.101730openalex-snapshot
Federated learning for sustainable intrusion detection systems: A review of green computing strategies and future directions
inferred← citeshttps://doi.org/10.1016/j.knosys.2025.114588openalex-snapshot
Gradient-based federated Bayesian optimization
inferred← citeshttps://doi.org/10.1038/s41598-026-44843-4openalex-snapshot
Optimization of cross-institutional medical federated learning framework driven by confidential computing
inferred← citeshttps://doi.org/10.1109/3scea68071.2026.11602969openalex-snapshot
Explainable Multi-Objective Optimization Reinforcement Learning for Privacy-Utility Tradeoff Management in IoT Systems
inferred← citeshttps://doi.org/10.1109/cogmi67134.2025.00048openalex-snapshot
FedHFT: Efficient Federated Fine-tuning with Heterogeneous Edge Clients
inferred← citeshttps://doi.org/10.1109/cvpr52734.2025.01922openalex-snapshot
FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning
inferred← citeshttps://doi.org/10.1109/iciscn67954.2026.11566196openalex-snapshot
Intelligent Privacy-Preserving Federated Learning with Multi-Objective Optimization for Communication-Efficient Systems
inferred← citeshttps://doi.org/10.1109/tbdata.2025.3534622openalex-snapshot
A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learning
inferred← citeshttps://doi.org/10.1109/tkde.2025.3555328openalex-snapshot
Ten Challenging Problems in Federated Foundation Models
inferred← citeshttps://doi.org/10.1109/trustcom66490.2025.00054openalex-snapshot
FlexiGrain: A Flexible and Fine-Grained Privacy Control Framework for Dynamic Social Networks
inferred← citeshttps://doi.org/10.1145/3652612openalex-snapshot
A Meta-Learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning
inferred← citeshttps://doi.org/10.1145/3803794openalex-snapshot
Towards Evolutionary Differential Privacy in Cross-Platform Spatial Crowdsourcing
inferred← citeshttps://doi.org/10.7717/peerj-cs.3042openalex-snapshot
Multi-objective optimization for smart cities: a systematic review of algorithms, challenges, and future directions
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.