Research map / Federated learning and privacy
Privacy attacks and secure learning: research map
364 accepted papers on Privacy attacks and secure learning in Federated learning and privacy, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 2 clusters and 6 approaches. The busiest year so far is 2026.
Within Federated learning and privacy, its share shrank from 17.6% in 2023–24 to 15.6% in 2025–26 (131 → 142 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).
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Approaches and key papers
preserving · vfl · encryption · 194 papers
Approaches in this cluster:
- Encryption-based private learning (105 papers)
Use homomorphic encryption and private synthetic data for privacy-preserving learning. - Private federated aggregation (50 papers)
Secure aggregation and encrypted gradients for efficient, fair federated learning. - Visual privacy and face de-identification (39 papers)
Obfuscate faces and private content in images while keeping utility.
Most cited and most cited since 2024:
- CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy (ICML 2016 · 1,289 citations)
- Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware (ICLR 2019 · 245 citations)
- Privacy-Preserving Face Recognition Using Trainable Feature Subtraction (CVPR 2024 · 33 citations)
- No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation (AAAI 2024 · 32 citations)
attack · defense · backdoor · 170 papers
Approaches in this cluster:
- Defending federated learning against poisoning (76 papers)
Detect and mitigate malicious clients and backdoors in federated learning with provable defenses. - Gradient inversion attacks (47 papers)
Reconstruct training data from shared gradients and defend against such leakage. - Membership inference and auditing (47 papers)
Attack and audit models for privacy leakage with membership inference and statistical tests.
Most cited and most cited since 2024:
- Inverting Gradients - How easy is it to break privacy in federated learning? (NeurIPS 2020 · 206 citations)
- Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective (CVPR 2021 · 168 citations)
- Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space (CVPR 2024 · 43 citations)
- Model Poisoning Attacks to Federated Learning via Multi-Round Consistency (CVPR 2025 · 28 citations)