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. 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.
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:2509.01253v2 Announce Type: replace-cross Abstract: In contemporary cloud-based services, protecting users' sensitive data and ensuring the confidentiality of the server's model are critical. F...
arXiv:2610. 35833v1 Announce Type: cross Abstract: In federated learning (FL), secure aggregation enables a server to compute the sum of local model updates of $N$ clients, without revealing any information on the individual model updates to the server, beyond their sum.
Pacifo is a framework that adapts fine‑tuning strategies to efficiently run the ResNet20 model using Fully Homomorphic Encryption (FHE). It introduces a public‑private layer partitioning scheme and an efficient ReLU approximation, reducing private layers and costly FHE operations during inference. Compared to state‑of‑the‑art FHE implementations, Pacifo achieves a 3.7–21.7× speedup while preserving 95–99 % of the original plaintext model accuracy.
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:2606. 07277v1 Announce Type: cross Abstract: Secure aggregation allows a server to aggregate users' local updates while preserving update privacy.
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: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.
HEAT introduces a fine‑tuning method that treats the number of iterations used to approximate nonlinearities in fully homomorphic encryption (FHE) as learnable parameters, allowing them to co‑adapt with model weights. By optimizing iteration counts per nonlinearity, HEAT reduces the required iterations, bootstraps, and overall latency for encrypted GPT‑2 decoding while improving decode agreement. The approach achieves a 3.1× reduction in iterations, a 1.6× reduction in bootstraps, and a 1.4× speed‑up in end‑to‑end latency without changing the model architecture or requiring retraining from scratch.
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.
Giskard is a scalable protocol for private and Byzantine‑robust decentralized aggregation in large‑scale learning. It organizes participants into a tree of committees of size O(log n) and computes a differentially private coordinate‑wise approximate median using secure multi‑party computation within each committee. Experiments with up to one million participants show that Giskard reduces per‑party communication by nearly three orders of magnitude compared to its closest competitors while maintaining robustness against up to n/4 Byzantine parties.