AdFL: In-Browser Federated Learning for Online Advertisement
This addresses privacy concerns for online publishers and users by enabling targeted ads without data sharing, though it is incremental as it applies existing federated learning techniques to a specific domain.
The paper tackles the problem of balancing targeted advertising with user privacy under regulations like GDPR by introducing AdFL, an in-browser federated learning framework that learns user ad preferences without sharing raw data. The result includes a proof-of-concept ad viewability prediction model achieving up to 92.59% AUC and demonstrating feasibility with minimal latency and modest performance declines when using differential privacy.
Since most countries are coming up with online privacy regulations, such as GDPR in the EU, online publishers need to find a balance between revenue from targeted advertisement and user privacy. One way to be able to still show targeted ads, based on user personal and behavioral information, is to employ Federated Learning (FL), which performs distributed learning across users without sharing user raw data with other stakeholders in the publishing ecosystem. This paper presents AdFL, an FL framework that works in the browsers to learn user ad preferences. These preferences are aggregated in a global FL model, which is then used in the browsers to show more relevant ads to users. AdFL can work with any model that uses features available in the browser such as ad viewability, ad click-through, user dwell time on pages, and page content. The AdFL server runs at the publisher and coordinates the learning process for the users who browse pages on the publisher's website. The AdFL prototype does not require the client to install any software, as it is built utilizing standard APIs available on most modern browsers. We built a proof-of-concept model for ad viewability prediction that runs on top of AdFL. We tested AdFL and the model with two non-overlapping datasets from a website with 40K visitors per day. The experiments demonstrate AdFL's feasibility to capture the training information in the browser in a few milliseconds, show that the ad viewability prediction achieves up to 92.59% AUC, and indicate that utilizing differential privacy (DP) to safeguard local model parameters yields adequate performance, with only modest declines in comparison to the non-DP variant.