arXiv:2606. 04399v1 Announce Type: new Abstract: In the paradigm of decentralized learning, a group of agents collaborate to train a global model using distributed datasets without a central server.
arXiv:2608.28198v1 Announce Type: new Abstract: Privacy-preserving learning is often motivated by the idea that protecting users' data can preserve trust and thus participation, improving utility in...
arXiv:2607. 23029v1 Announce Type: cross Abstract: Federated learning enables collaborative model training across distributed clients without centralising their data, yet privacy remains a persistent concern because the shared model updates can leak information about local datasets.
arXiv:2509. 10691v3 Announce Type: replace-cross Abstract: Decentralized federated learning enables collaborative model training without a central server, but shared model updates can still leak sensitive information through inversion, reconstruction, and membership inference attacks.
arXiv:2603. 19040v2 Announce Type: replace Abstract: Differentially private wireless federated learning (DPWFL) is a promising framework for protecting sensitive user data.
arXiv:2606. 01952v1 Announce Type: new Abstract: As reinforcement learning (RL) increasingly applies to sensitive domains, such as health care and recommendation systems, privacy-preserving techniques have become essential to protect users' sensitive information.
The paper introduces a general learning framework that protects privacy in federated learning by distorting model parameters, enabling a trade‑off between privacy and utility. The algorithm supports arbitrary privacy measurements and delivers personalized utility‑privacy balances for each parameter, client, and communication round. The authors prove that the gap between their algorithm’s utility loss and the optimal loss is sub‑linear in iterations, provide a convergence rate, and demonstrate empirically that their method outperforms baselines under the same privacy budget.
arXiv:2407. 08233v3 Announce Type: replace Abstract: Current differentially private learning paradigms face a severe utility bottleneck: DP-SGD degrades performance through noise accumulation over training steps, while aggregation-based approaches such as PATE suffer from data inefficiency due to disjoint data partitioning.
SPADE-DFL is a communication‑efficient decentralized federated learning algorithm that uses a primal–dual method to allow the number of local function‑value updates between neighbor exchanges to increase with the computation budget while maintaining non‑private convergence rates. For smooth nonconvex objectives, it achieves a time‑averaged stationarity and consensus bound of ≠O(T−1/3) with only ≠Theta(T−2/3) communication rounds, where T is the number of local updates per client. The method also supports client‑level differential privacy by isolating data‑dependent increments, proving privacy for the full interactive transcript and quantifying the resulting optimization error, and demonstrates higher mean test accuracy than existing decentralized learning methods on four classification tasks.
arXiv:2602. 06838v3 Announce Type: replace Abstract: Federated learning enables collaborative model training across distributed clients while preserving data privacy.
Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets ($\varepsilon_i$) according to institutional policies and data sensitivity. In practice, many HDP-FL systems employ $\varepsilon$-aware server aggregation to improve model utility by re-weighting client updates according to their declared privacy budgets.
arXiv:2609.27658v1 Announce Type: new Abstract: Private decentralized learning is affected by sampling noise, privacy noise, and decentralized bias under heterogeneous data. We propose Private Recurs...