arXiv:2605. 30123v2 Announce Type: replace-cross Abstract: Homomorphic encryption (HE) enables privacy-preserving aggregation in federated learning (FL) by allowing the server to operate on encrypted data without decryption.
arXiv:2609. 01945v1 Announce Type: cross Abstract: Federated Learning enables multiple clients to train a shared model while keeping their local datasets isolated.
arXiv:2607. 20890v1 Announce Type: new Abstract: On-device federated learning (FL) enables privacy-preserving and personalized model training on resource-constrained devices such as smartphones and IoT nodes.
arXiv:2606. 07277v1 Announce Type: cross Abstract: Secure aggregation allows a server to aggregate users' local updates while preserving update privacy.
arXiv:2606. 10780v1 Announce Type: cross Abstract: Secure aggregation is a vital component for mitigating gradient leakage in federated learning, but its communication cost conventionally scales with the gradient dimension.
arXiv:2607. 06612v1 Announce Type: cross Abstract: Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality, thereby enhancing user privacy.
arXiv:2607. 04218v1 Announce Type: new Abstract: The growing demand for privacy-preserving, data-intensive applications such as IoT, augmented reality, and autonomous systems positions Federated Learning (FL) as a key enabler in 6G networks.
Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality, thereby enhancing user privacy. However, traditional FL frameworks rely on a centralized aggregation server and assume honest-but-curious clients, making them susceptible to both server-side inference and client-side poisoning attacks.
arXiv:2606. 26822v1 Announce Type: new Abstract: Federated Learning (FL) has become a foundational paradigm for privacy-preserving distributed intelligence, yet its scalability remains fundamentally constrained by communication bottlenecks, device heterogeneity, and the challenges of training under statistically non-IID data.
The paper investigates how federated learning (FL) updates in vehicular edge networks can reveal client identities through gradient-based attacks on inertial sensor data, using the UCI Human Activity Recognition benchmark as a proxy. Experiments show that an honest-but-curious server can identify clients with near-perfect accuracy from unprotected updates. The authors evaluate lightweight defenses—clipping followed by Gaussian noise and ensemble FL—to mitigate this privacy risk while preserving model utility, reporting differential‑privacy budgets and empirical results across multiple attack classifiers and data partitions.
SecureDrive‑FL combines differential privacy (DP‑SGD) with a novel Gradient‑Aware Selective Homomorphic Encryption (GASHE) scheme to protect federated driver‑monitoring models. GASHE encrypts only gradient components that exceed a DP‑calibrated sensitivity threshold, avoiding full‑parameter encryption. In experiments on a ten‑class distracted driver task, SecureDrive‑FL matches DP‑SGD’s poisoning resistance while also defending against Man‑in‑the‑Middle attacks, adding only 8–10% runtime overhead.
arXiv:2405. 07708v3 Announce Type: replace Abstract: Decentralized learning (DL) enables participants to collaboratively train models without a central server, yet it faces significant scalability challenges that demand sparsification to reduce the prohibitive communication costs of peer-to-peer exchange.
The paper demonstrates that in decentralized federated learning, secure aggregation implemented through local neighborhood aggregation can leak private model updates. By exploiting the asymmetric aggregate views available to colluding semi‑honest nodes, the authors formulate the problem as a Hidden Subset Sum Problem and develop a lattice‑based reconstruction attack. Experiments on image, tabular, and text datasets show that attackers can recover honest participants’ updates and reconstruct private training data.