We consider the problem of collaborative personalized mean estimation under a privacy constraint in an environment of several agents continuously receiving data according to arbitrary unknown agent-specific distributions. In particular, we provide a method based on hypothesis testing coupled with differential privacy and data variance estimation. Two differential privacy mechanisms protecting the releases of each agent’s current sample mean and two data variance estimation schemes are proposed, and we provide a theoretical convergence analysis of the proposed algorithm for any bounded unknown distributions on the agents’ data, showing that collaboration provides faster convergence than a fully local approach where agents do not share data. Moreover, we provide analytical performance curves for the case with an oracle class estimator, i.e., the class structure of the agents, where agents receiving data from distributions with the same mean are considered to be in the same class, is known. The theoreticalfaster-than-localconvergence guarantee is backed up by extensive numerical results showing that for a considered scenario with 200 agents from two or three classes the proposed approach indeed converges much faster than a fully local approach, and performs comparably to the ideal (all-data-public) case. This illustrates the benefit of private collaboration in an online setting.

We consider the problem of collaborative personalized mean estimation under a privacy constraint in an environment of several agents continuously receiving data according to arbitrary unknown agent-specific distributions. In particular, we provide a method based on hypothesis testing coupled with differential privacy. Two privacy mechanisms are proposed and we provide a theoretical convergence analysis of the proposed algorithm for any bounded unknown distributions on the agents’ data. Numerical results show that for a considered scenario the proposed approach converges much faster than a fully local approach where agents do not share data, and performs comparably to ideal performance where all data is public. This illustrates the benefit of private collaboration in an online setting.

We consider straggler resiliency in decentralized learning using stochastic gradient descent under the notion of network differential privacy (DP). In particular, we extend the recently proposed framework of privacy amplification by decentralization by Cyffers and Bellet to include training latency—comprising both computation and communication latency. Analytical results on both the convergence speed and the DP level are derived for training over a logical ring for both a skipping scheme (which ignores the stragglers after a timeout) and a baseline scheme that waits for each node to finish before the training continues. Our results show a trade-off between training latency, accuracy, and privacy, parameterized by the timeout of the skipping scheme. Finally, results when training a logistic regression model on a real-world dataset are presented.

We propose to extend the concept of private information retrieval by allowing for distortion in the retrieval process and relaxing the perfect privacy requirement at the same time. In particular, we study the tradeoff between download rate, distortion, and user privacy leakage, and show that in the limit of large file sizes this tradeoff can be captured via a novel information-theoretical formulation for datasets with a known distribution. Moreover, for scenarios where the statistics of the dataset is unknown, we propose a new deep learning framework by leveraging a generative adversarial network approach, which allows the user to learn efficient schemes from the data itself, minimizing the download cost. We evaluate the performance of the scheme on a synthetic Gaussian dataset as well as on the MNIST, CIFAR-10, and LSUN datasets. For the MNIST, CIFAR-10, and LSUN datasets, the data-driven approach significantly outperforms a nonlearning-based scheme which combines source coding with multiple file download.

The beamforming technique is applied in 5G NR systems to enhance the receiving signal power, especially for millimeter-wave communications. Beamforming complicates the paging procedure and brings extra overhead to the transmission of paging messages. In order to cover the serving area, the gNB should transmit broadcast control messages via beam sweeping. However, beam sweeping is inefficient for paging operation. In this work, an intelligent directional paging framework is proposed. The proposed beam-based paging list enables the gNB to page UEs flexibly. With the implicit gathering of the UE information, the gNB can make intelligent paging decisions without beam sweeping, decrease the paging resources consumption, and optimize the system paging delay. The proposed Markov chain based analytical model is validated by the simulation results. The results show that the proposed schemes outperform the NR baseline in successful paging rate, delay, and system capacity.