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Belt and Braces: When Federated Learning Meets Differential Privacy

https://doi.org/10.1145/3650028
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What this work says

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

inferredcites →https://doi.org/10.1007/978-3-030-59410-7_33openalex-snapshot
FedSel: Federated SGD Under Local Differential Privacy with Top-k Dimension Selection
inferredcites →https://doi.org/10.1016/j.inffus.2020.07.009openalex-snapshot
Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy
inferredcites →https://doi.org/10.1109/sp.2017.41openalex-snapshot
Membership Inference Attacks Against Machine Learning Models
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/tpami.2021.3107796openalex-snapshot
A(DP)^2SGD: Asynchronous Decentralized Parallel Stochastic Gradient Descent with Differential Privacy
inferredcites →https://doi.org/10.1109/tpami.2023.3332428openalex-snapshot
Multi-Stage Asynchronous Federated Learning With Adaptive Differential Privacy
inferredcites →https://doi.org/10.1145/1866739.1866758openalex-snapshot
A firm foundation for private data analysis
inferredcites →https://doi.org/10.1145/2666468openalex-snapshot
Privacy Aware Learning
inferredcites →https://doi.org/10.1145/2810103.2813687openalex-snapshot
Privacy-Preserving Deep 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/10.1145/3318464.3380596openalex-snapshot
Cryptϵ
inferredcites →https://doi.org/10.1145/3387107openalex-snapshot
Federated learning for privacy-preserving AI
inferredcites →https://doi.org/10.1145/3418290openalex-snapshot
DP-cryptography
inferredcites →https://doi.org/10.1145/3460120.3484756openalex-snapshot
When Machine Unlearning Jeopardizes Privacy
inferredcites →https://doi.org/10.1145/3460120.3484794openalex-snapshot
On the Rényi Differential Privacy of the Shuffle Model
inferredcites →https://doi.org/10.1145/3470449openalex-snapshot
Artificial intelligence across company borders
inferredcites →https://doi.org/10.1145/3624010openalex-snapshot
A Survey of Privacy Attacks in Machine Learning
inferredcites →https://doi.org/10.14778/2350229.2350253openalex-snapshot
Functional mechanism
inferredcites →https://doi.org/10.1561/0400000042openalex-snapshot
The Algorithmic Foundations of Differential Privacy
inferredcites →https://doi.org/10.1561/2200000083openalex-snapshot
Advances and Open Problems in Federated Learning
inferredcites →https://doi.org/10.1609/aaai.v35i10.17053openalex-snapshot
FLAME: Differentially Private Federated Learning in the Shuffle Model
inferredcites →https://doi.org/10.24963/ijcai.2021/216openalex-snapshot
Federated Model Distillation with Noise-Free Differential Privacy
inferredcites →https://doi.org/10.24963/ijcai.2021/217openalex-snapshot
LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy
inferredcites →https://doi.org/10.48550/arxiv.1602.05629openalex-snapshot
Communication-Efficient Learning of Deep Networks from Decentralized Data
inferredcites →https://doi.org/10.48550/arxiv.1610.05492openalex-snapshot
Federated Learning: Strategies for Improving Communication Efficiency
inferredcites →https://doi.org/10.48550/arxiv.2006.15429openalex-snapshot
Understanding Gradient Clipping in Private SGD: A Geometric Perspective
inferredcites →https://doi.org/10.48550/arxiv.2103.00039openalex-snapshot
Practical and Private (Deep) Learning without Sampling or Shuffling

What points at this work

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

inferred← citeshttps://doi.org/10.1007/978-3-032-28097-8_3openalex-snapshot
Assessing Risks and Privacy Challenges in the Artificial Intelligence Era
inferred← citeshttps://doi.org/10.1007/s10462-026-11574-xopenalex-snapshot
The application of federated learning in breast cancer diagnosis and treatment: a survey
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.engappai.2025.113614openalex-snapshot
Federated learning for big data: A survey on opportunities, applications, and future directions
inferred← citeshttps://doi.org/10.1038/s41598-025-12575-6openalex-snapshot
ALDP-FL for adaptive local differential privacy in federated learning
inferred← citeshttps://doi.org/10.1109/access.2024.3523909openalex-snapshot
A Survey of Differential Privacy Techniques for Federated Learning
inferred← citeshttps://doi.org/10.1109/ccnc65079.2026.11366332openalex-snapshot
A Unified Lightweight Benchmark for Privacy-Preserving Federated Learning in Cyber-Physical Systems (Fashion-MNIST Case Study)
inferred← citeshttps://doi.org/10.1109/dcoss-iot69657.2026.00089openalex-snapshot
Efficient Hierarchical Federated Learning with Dynamic Cluster Formation for IoT Wearable Analytics
inferred← citeshttps://doi.org/10.1109/edgecom66327.2025.00013openalex-snapshot
A Federated Edge Network Orchestration And Privacy-enhanced Collaborative Inference Architecture Based On KubeEdge
inferred← citeshttps://doi.org/10.1109/eei67650.2025.11334988openalex-snapshot
Optimization of Differential Privacy Mechanisms in Federated Learning from a Non-Cooperative Game Perspective
inferred← citeshttps://doi.org/10.1109/issre66568.2025.00053openalex-snapshot
Enhancing Privacy and Robustness in Federated Learning with Local Data Distribution Invariance and Byzantine-Resilient Aggregation
inferred← citeshttps://doi.org/10.1109/iswta68114.2025.11329774openalex-snapshot
A Survey on Real Time Face Detection and Recognition in Video Streams for Multimedia Communication Systems using YOLOv12 Algorithms
inferred← citeshttps://doi.org/10.1109/jiot.2025.3558910openalex-snapshot
Optimizing Federated Learning: Addressing Key Challenges in Real-World Applications
inferred← citeshttps://doi.org/10.1109/nnice68970.2026.11465223openalex-snapshot
Differentially Private Federated Learning with Adaptive Clipping Thresholds
inferred← citeshttps://doi.org/10.1109/ojcoms.2025.3566464openalex-snapshot
PP-QADMM: A Dual-Driven Perturbation and Quantized ADMM for Privacy Preserving and Communication-Efficient Federated Learning
inferred← citeshttps://doi.org/10.1109/tmc.2025.3574784openalex-snapshot
FedDSV: Shapley Value-Based Contribution Estimation in Federated Learning With Dynamic Participation
inferred← citeshttps://doi.org/10.1109/tnnls.2026.3680142openalex-snapshot
EMM-Det: Energy-Efficient Multidrone Tiny Object Detection by Memory-Enhanced Spiking Neural Networks
inferred← citeshttps://doi.org/10.1109/tpami.2026.3697332openalex-snapshot
Privacy-Preserving Online Federated Learning for Massive Infinite Streams
inferred← citeshttps://doi.org/10.1111/exsy.70096openalex-snapshot
Federated Learning: Concepts, Challenges and Implementation
inferred← citeshttps://doi.org/10.1145/3729706.3729712openalex-snapshot
Enhancing the Security of Word Embedding in Machine Learning as a Service against Reverse Engineering Attacks using Homomorphic Encryption
inferred← citeshttps://doi.org/10.1145/3801079openalex-snapshot
Differentially Private Federated Learning: A Systematic Review
inferred← citeshttps://doi.org/10.2139/ssrn.7439221openalex-snapshot
Aligning incentives in core-periphery industrial data spaces: regulatory pathways for SME participation and profit sharing
inferred← citeshttps://doi.org/10.3390/electronics14163237openalex-snapshot
HiSatFL: A Hierarchical Federated Learning Framework for Satellite Networks with Cross-Domain Privacy Adaptation
inferred← citeshttps://doi.org/10.3390/fi18030148openalex-snapshot
Differentially Private Federated Learning with Adaptive Clipping Thresholds
inferred← citeshttps://doi.org/10.4236/jcc.2026.148002openalex-snapshot
Privacy, Security, and Trust Challenges in Federated Learning: A Systematic Review and Future Research Agenda
inferred← citeshttps://doi.org/10.4236/jis.2026.174018openalex-snapshot
Evaluating Data-Protection Mechanisms in Federated Learning: A Performance-Centric Analysis
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