Recommendation is crucial for both user experience and company revenue in Meituan as a leading lifestyle company, and generative recommendation models (GRMs) are shown to produce quality recommendations recently. However, existing systems are limited by insufficient functionality support and inefficient implementations for training GRMs in industrial scenarios. As such, we introduce MTGenRec as an efficient and scalable system for GRM training. Specifically, to handle real-time insertions/deletions of sparse embeddings, MTGenRec employs dynamic hash tables to replace static ones. To improve training efficiency, MTGenRec conducts dynamic sequence balancing to address the computation load imbalances among GPUs and adopts feature ID deduplication alongside automatic table merging to accelerate embedding lookup. Extensive experiments show that MTGenRec improves training throughput by 1.6x - 2.4x while achieving good scalability when running over 100 GPUs. MTGenRec has been deployed for many applications in Meituan and is now handling hundreds of millions of requests on a daily basis. On the delivery platform, we observe a 1.22% growth in user order volume and a 1.31% enhancement in online PV_CTR.

Vertical multimodal federated learning (VMFL) enables multiple clients holding data from different modalities to conduct collaboratively model training. Existing methods typically assume that multimodal data samples (i.e., text and image) from the same entity (i.e., person) are paired across the clients (i.e., aligned). However, this assumption rarely holds in practice, as data is often collected independently with no shared identifiers. To address this challenge, we propose hashing-based alignment (HAL), a new VMFL framework that works without pre-aligned samples. HAL consists of two key components. The first component is an efficient and privacy-preserving method to identify similar samples from different modalities as aligned pairs. It adopts locality sensitive hashing (LSH) for the efficient retrieval of similar samples, introduces a shift-orthogonal hashing scheme to tackle the gaps between different modalities, and uses a bloom-style method for secure Hamming distance estimation. We prove that the shift-orthogonal hashing reduces distance estimation errors and secure Hamming distance estimation satisfies differential privacy. The second component is a neighbor-aware fusion strategy, which applies cross-attention to aggregate informative signals from the aligned samples without relying on explicit similarity scores. Experimental results on two real-world datasets show that compared with five state-of-the-art (SOTA) baselines, HAL improves the cross-modal retrieval accuracy by over 63%, while also achieving up to 154× speedup.

Due to the rising concerns on privacy protection, how to build machine learning models from distributed databases with privacy guarantees has gained more popularity. Vertical federated learning (VFL) trains machine learning models in a privacy-preserving way when the data features are scattered over distributed databases. We study the participant selection problem (PSP) for VFL, which chooses a given number of participants to conduct training while maximizing model accuracy. Compared to training with all participants, PSP can filter out hitch-riders that contribute marginally to model quality and reduce training time by involving fewer participants. To achieve good model accuracy, we formulate PSP as choosing a set of participants that maximizes the likelihood of the data samples. Then, utilizing the k-nearest neighbors (KNN) classifier as the proxy model, we express the likelihood as a function of the selected participants and prove that the function is sub modular. The submodular property is favorable as it can account for the feature diversity among the participants and allows to greedily select the participant with the maximum gain in each step. However, the selection process requires finding the top-k neighbors of a data sample as the basic operation, which is expensive in VFL setting as it involves encrypted communication. As such, we adapt the Fagin's algorithm, a famous top-k query algorithm, to reduce the amount of encrypted communication. We deploy our solution VFPS-SM across five distributed nodes and conduct experiments with 10 datasets and 3 models to evaluate its performance. The results show that VFPS-SM can reduce the end-to-end running time by up to$35\times$, selection time$365\times$and improve model accuracy by 6.0% compared with state-of-the-art baselines.

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×.