Practical Federated Recommendation Model Learning Using ORAM with Controlled Privacy

dc.contributor.authorLiu, Jinyuen
dc.contributor.authorXiong, Wenjieen
dc.contributor.authorSuh, G. Edwarden
dc.contributor.authorMaeng, Kiwanen
dc.date.accessioned2025-04-04T12:13:58Zen
dc.date.available2025-04-04T12:13:58Zen
dc.date.issued2025-03-30en
dc.date.updated2025-04-01T07:47:48Zen
dc.description.abstractTraining high-quality recommendation models requires collecting sensitive user data. The popular privacy-enhancing training method, federated learning (FL), cannot be used practically due to these models’ large embedding tables. This paper introduces FEDORA, a system for training recommendation models with FL. FEDORA allows each user to only download, train, and upload a small subset of the large tables based on their private data, while hiding the access pattern using oblivious memory (ORAM). FEDORA reduces the ORAM’s prohibitive latency and memory overheads by (1) introducing 𝜖-FDP, a formal way to balance the ORAM’s privacy with performance, and (2) placing the large ORAM in a power- and cost-efficient SSD with SSD-friendly optimizations. Additionally, FEDORA is carefully designed to support (3) modern operation modes of FL. FEDORA achieves high model accuracy by using private features during training while achieving, on average, 5× latency and 158× SSD lifetime improvement over the baseline.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1145/3676641.3716014en
dc.identifier.urihttps://hdl.handle.net/10919/125145en
dc.language.isoenen
dc.publisherACMen
dc.rightsIn Copyrighten
dc.rights.holderThe author(s)en
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titlePractical Federated Recommendation Model Learning Using ORAM with Controlled Privacyen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
3676641.3716014.pdf
Size:
2.1 MB
Format:
Adobe Portable Document Format
Description:
Published version
License bundle
Now showing 1 - 1 of 1
Name:
license.txt
Size:
1.5 KB
Format:
Item-specific license agreed upon to submission
Description: