Collaborating Authors

Federated Learning


The five interns all look up. Brad, a burly caucasian jock, waves hello overenthusiastically. Kai, a nonbinary Japanese-American hacker, plays with a Rubix cube. Devi, a bubbly Indian-American networker, snaps a selfie. Mateo, a scrawny Hispanic bookworm, pauses in the middle of eating a sandwich. Aliyah, a sharply-dressed African-American security enthusiast, looks unimpressed.

Privacy Threats Against Federated Matrix Factorization Machine Learning

Matrix Factorization has been very successful in practical recommendation applications and e-commerce. Due to data shortage and stringent regulations, it can be hard to collect sufficient data to build performant recommender systems for a single company. Federated learning provides the possibility to bridge the data silos and build machine learning models without compromising privacy and security. Participants sharing common users or items collaboratively build a model over data from all the participants. There have been some works exploring the application of federated learning to recommender systems and the privacy issues in collaborative filtering systems. However, the privacy threats in federated matrix factorization are not studied. In this paper, we categorize federated matrix factorization into three types based on the partition of feature space and analyze privacy threats against each type of federated matrix factorization model. We also discuss privacy-preserving approaches. As far as we are aware, this is the first study of privacy threats of the matrix factorization method in the federated learning framework.

Towards Causal Federated Learning For Enhanced Robustness and Privacy Artificial Intelligence

Federated Learning is an emerging privacy-preserving distributed machine learning approach to building a shared model by performing distributed training locally on participating devices (clients) and aggregating the local models into a global one. As this approach prevents data collection and aggregation, it helps in reducing associated privacy risks to a great extent. However, the data samples across all participating clients are usually not independent and identically distributed (noni.i.d.), and Out of Distribution (OOD) generalization for the learned models can be poor. Besides this challenge, federated learning also remains vulnerable to various attacks on security wherein a few malicious participating entities work towards inserting backdoors, degrading the generated aggregated model as well as inferring the data owned by participating entities. In this paper, we propose an approach for learning invariant (causal) features common to all participating clients in a federated learning setup and analyse empirically how it enhances the Out of Distribution (OOD) accuracy as well as the privacy of the final learned model.

A Survey towards Federated Semi-supervised Learning Machine Learning

The success of Artificial Intelligence (AI) should be largely attributed to the accessibility of abundant data. However, this is not exactly the case in reality, where it is common for developers in industry to face insufficient, incomplete and isolated data. Consequently, federated learning was proposed to alleviate such challenges by allowing multiple parties to collaboratively build machine learning models without explicitly sharing their data and in the meantime, preserve data privacy. However, existing algorithms of federated learning mainly focus on examples where, either the data do not require explicit labeling, or all data are labeled. Yet in reality, we are often confronted with the case that labeling data itself is costly and there is no sufficient supply of labeled data. While such issues are commonly solved by semi-supervised learning, to the best of knowledge, no existing effort has been put to federated semi-supervised learning. In this survey, we briefly summarize prevalent semi-supervised algorithms and make a brief prospect into federated semi-supervised learning, including possible methodologies, settings and challenges.

A Demonstration of Smart Doorbell Design Using Federated Deep Learning Artificial Intelligence

Smart doorbells have been playing an important role in protecting Furthermore, the processing and storage of multiple video streams our modern homes. Existing approaches of sending video streams make the subscription more costly. Secondly, this design requires to a centralized server (or Cloud) for video analytics have been a huge amount of reliable bandwidth, which may not always be facing many challenges such as latency, bandwidth cost and more had. Third, even if we assume that we could address latency and importantly users' privacy concerns. To address these challenges, bandwidth issue by empowering a sophisticated infrastructure, a this paper showcases the ability of an intelligent smart doorbell large class of video-based applications may not be suitable because based on Federated Deep Learning, which can deploy and manage of regulations and security concerns of sharing data as there is an video analytics applications such as a smart doorbell across Edge involvement of biometric data of residents.