Vertical federated learning (VFL) trains model when the features of data samples are scattered over multiple clients. To improve efficiency, a promising approach is to find a coreset of the data samples and use it as a smaller training set. However, existing methods produce a large coreset when there are many clients and have long running time. To address these problems, we propose HaCore for efficient coreset construction in VFL setting. HaCore first employs locality sensitive hashing (LSH) to map features to bit signatures locally on the clients, and then merges the local signatures for k-medoids clustering. Data samples that correspond to the medoids are added to the coreset. The core idea is that the distance of original data samples can be approximated by the Hamming distance between their LSH-based bit signatures. To accelerate k-medoids, we utilize an inverted index to search the nearest medoid and a bit-counting method to quickly compute the aggregate distance from many signatures to a medoid. We evaluate HaCore on 5 datasets and compare with state-of-the-art coreset construction methods for VFL. The results show that HaCore accelerates the best-performing baseline by over 45x and matches the accuracy of training with all samples.

Split learning is a prominent framework for vertical federated learning, where multiple clients collaborate with a central server for model training by exchanging intermediate embeddings. Recently, it is shown that an adversarial server can exploit the intermediate embeddings to train surrogate models to replace the bottom models on the clients (i.e., model stealing). The surrogate models can also be used to reconstruct private training data of the clients (i.e., data stealing). To defend against these stealing attacks, we propose Model Rake (i.e., Rake), which runs two bottom models on each client and differentiates their output spaces to make the two models distinct. Rake hinders the stealing attacks because it is difficult for a surrogate model to approximate two distinct bottom models. We prove that, under some assumptions, the surrogate model converges to the average of the two bottom models and thus will be inaccurate. Extensive experiments show that Rake is much more effective than existing methods in defending against both model and data stealing attacks, and the accuracy of normal model training is not affected.

Vertical federated learning (VFL) trains models when multiple databases (a.k.a participants) hold different features of the same set of samples. By quantifying each participant's contribution to model training, data valuation can prevent hitch-riders and reward the instrumental parties. However, vertical federated data valuation (VFDV) is challenging because it needs to be accurate and efficient while protecting participant data privacy. In this paper, we propose a method meeting all three requirements by using projection and sampling for mutual information estimation (thus dubbed PS-MI). In particular, we first show that the utility of a participant set (a.k.a a coalition ) can be expressed as the mutual information (MI) between their features and the target labels. MI is favorable because it does not depend on the model to train (i.e., model-agnostic ) and can be estimated via k -nearest neighbor (KNN). To run KNN, instead of using costly homomorphic encryption to protect data privacy, we apply simple random projection to participant features before distance computation. We prove that random projection ensures differential privacy and preserves unbiased distance estimates. Since the contribution of a participant involves many coalitions, we adopt stratified sampling to reduce the number of coalitions while controlling estimation variance. To further improve efficiency, we incorporate optimizations including using locality sensitive hashing (LSH) to prune kNN candidates, batching kNN candidate checking for multiple coalitions, and adaptive early termination for utility evaluation. We compare PS-MI with 5 state-of-the-art VFDV methods. The results show that PS-MI yields higher accuracy and shorter running time than the baselines, and the maximum speedup can be 592×.

Internet of Things (IoT) systems in general consist of a lot of devices with massive connectivity. Those devices are usually constrained with limited energy supply and can only operate at low power and low rate. One solution to limited energy is to use energy harvesting to provide sustainable energy. The set of technologies adopted in next-generation wireless communication systems, such as massive MIMO and Non-Orthogonal Multiple Access (NOMA), can provide solutions to increase the throughput of IoT systems. In this paper we investigate a cellular-based IoT system combined with energy harvesting and NOMA. We consider all base stations (BS) and IoT devices follow the Poisson Point Process (PPP) distribution in a given area. The unit time slot is divided into two phases, energy harvesting phase in downlink (DL) and data transmission phase in uplink (UL). That is, IoT devices will first harvest energy from all BS transmissions and then use the harvested energy to do the NOMA information transmission. We define an energy harvesting circle within which all IoT devices can harvest enough energy for NOMA transmission. The design objective is to maximize the total throughput in UL within the circle by varying the duration T of energy harvesting phase. In our work, we also consider the inter-cell interference in the throughput calculation. The analysis of Probability Mass Function (PMF) for IoT devices in the energy harvesting circle is also compared with simulation results. It is shown that the BS density needs to be carefully set so that the IoT devices in the energy harvesting circle receive relatively smaller interference and energy circles overlap only with small probability. Our simulations show that there exists an optimal T to achieve maximum throughput. When the BSs are densely deployed consequently the total throughput will decrease because of the interference.