Federated Topic Modeling

Jiang, Di; Song, Yuanfeng; Tong, Yongxin; Wu, Xueyang; Zhao, Weiwei; Xu, Qian; Yang, Qiang

doi:10.1145/3357384.3357909

Cited by 38 publications

(21 citation statements)

References 27 publications

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“…Yang et al presented concepts (forming a new ontology) and methods on employing federated learning in machine learning applications [68]. Jiang et al proposed federated topic modeling that applied federated machine learning for topic models [25]. Wang et al developed a local diferential privacy based framework for federated latent Dirichlet allocation [64].…”

Section: Federated Machine Learningmentioning

confidence: 99%

Federated Dynamic Graph Neural Networks with Secure Aggregation for Video-based Distributed Surveillance

Jiang

Jung

Karl

et al. 2022

ACM Trans. Intell. Syst. Technol.

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Distributed surveillance systems have the ability to detect, track, and snapshot objects moving around in a certain space. The systems generate video data from multiple personal devices or street cameras. Intelligent video-analysis models are needed to learn dynamic representation of the objects for detection and tracking. Can we exploit the structural and dynamic information, without storing the spatiotemporal video data at a central server that leads to a violation of user privacy? In this work, we introduce Federated Dynamic Graph Neural Network (Feddy), a distributed and secured framework to learn the object representations from graph sequences: (1) It aggregates structural information from nearby objects in the current graph as well as dynamic information from those in the previous graph. It uses a self-supervised loss of predicting the trajectories of objects. (2) It is trained in a federated learning manner. The centrally located server sends the model to user devices. Local models on the respective user devices learn and periodically send their learning to the central server without ever exposing the user’s data to server. (3) Studies showed that the aggregated parameters could be inspected though decrypted when broadcast to clients for model synchronizing, after the server performed a weighted average. We design an appropriate aggregation mechanism of secure aggregation primitives that can protect the security and privacy in federated learning with scalability. Experiments on four video camera datasets as well as simulation demonstrate that Feddy achieves great effectiveness and security.

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Section: Federated Machine Learningmentioning

confidence: 99%

Federated Dynamic Graph Neural Networks with Secure Aggregation for Video-based Distributed Surveillance

Jiang

Jung

Karl

et al. 2022

ACM Trans. Intell. Syst. Technol.

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show abstract

“…Since the model updates contain much less information than the raw user data, federated learning can provide an efective way to exploit the private data of diferent users and protect their privacy at the same time [26]. Based on the idea of federated learning, Jiang et al [14] proposed a federated topic modeling approach to train topic models from the corpus owned by diferent parties. In these federated learning methods, the samples for model training are distributed on diferent clients, and each client shares the same feature space, aka horizontal federated learning [42].…”

Section: Federated Learningmentioning

confidence: 99%

FedCTR: Federated Native Ad CTR Prediction with Cross-platform User Behavior Data

Lyu³

et al. 2022

ACM Trans. Intell. Syst. Technol.

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Native ad is a popular type of online advertisement which has similar forms with the native content displayed on websites. Native ad click-through rate (CTR) prediction is useful for improving user experience and platform revenue. However, it is challenging due to the lack of explicit user intent, and user behaviors on the platform with native ads may be insufficient to infer users’ interest in ads. Fortunately, user behaviors exist on many online platforms which can provide complementary information for user interest mining. Thus, leveraging multi-platform user behaviors is useful for native ad CTR prediction. However, user behaviors are highly privacy-sensitive, and the behavior data on different platforms cannot be directly aggregated due to user privacy concerns and data protection regulations. Existing CTR prediction methods usually require centralized storage of user behavior data for user modeling, which cannot be directly applied to the CTR prediction task with multi-platform user behaviors. In this paper, we propose a federated native ad CTR prediction method named FedCTR, which can learn user interest representations from cross-platform user behaviors in a privacy-preserving way. On each platform a local user model learns user embeddings from the local user behaviors on that platform. The local user embeddings from different platforms are uploaded to a server for aggregation, and the aggregated ones are sent to the ad platform for CTR prediction. Besides, we apply local differential privacy (LDP) and differential privacy (DP) to the local and aggregated user embeddings respectively for better privacy protection. Moreover, we propose a federated framework for collaborative model training with distributed models and user behaviors. Extensive experiments on real-world dataset show that FedCTR can effectively leverage multi-platform user behaviors for native ad CTR prediction in a privacy-preserving manner.

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“…To strengthen the security, devices can prevent data leakage against an honest-but-curious server that aggregates encrypted/masked gradients from devices. [9] It also applies to release non-IIDness across devices in topic modeling [10]. However, these FL-based algorithms have no guidelines in accordance with data distribution among the devices.…”

Section: Related Workmentioning

confidence: 99%

Hiding in the Crowd: Federated Data Augmentation for On-Device Learning

Jeong

Park

et al. 2021

IEEE Intell. Syst.

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To cope with the lack of on-device machine learning samples, this article presents a distributed data augmentation algorithm, coined federated data augmentation (FAug). In FAug, devices share a tiny fraction of their local data, i.e., seed samples, and collectively train a synthetic sample generator that can augment the local datasets of devices. To further improve FAug, we introduce a multi-hop based seed sample collection method and an oversampling technique that mixes up collected seed samples. Both approaches enjoy the benefit from the crowd of devices, by hiding data privacy from preceding hops and feeding diverse seed samples. In the image classification tasks, simulations demonstrate that the proposed FAug frameworks yield stronger privacy guarantees, lower communication latency, and higher on-device ML accuracy.

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Federated Topic Modeling

Cited by 38 publications

References 27 publications

Federated Dynamic Graph Neural Networks with Secure Aggregation for Video-based Distributed Surveillance

Federated Dynamic Graph Neural Networks with Secure Aggregation for Video-based Distributed Surveillance

FedCTR: Federated Native Ad CTR Prediction with Cross-platform User Behavior Data

Hiding in the Crowd: Federated Data Augmentation for On-Device Learning

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