Skip to Main content Skip to Navigation
Conference papers

The More the Merrier - Federated Learning from Local Sphere Recommendations

Abstract : With Google’s Federated Learning & Facebook’s introduction of client-side NLP into their chat service, the era of client-side Machine Learning is upon us. While interesting ML approaches beyond the realm of toy examples were hitherto confined to large data-centers and powerful GPU’s, exponential trends in technology and the introduction of billions of smartphones enable sophisticated processing swarms of even hand-held devices. Such approaches hold several promises: 1. Without the need for powerful server infrastructures, even small companies could be scalable to millions of users easily and cost-efficiently; 2. Since data only used in the learning process never need to leave the client, personal information can be used free of privacy and data security concerns; 3. Since privacy is preserved automatically, the full range of personal information on the client device can be utilized for learning; and 4. without round-trips to the server, results like recommendations can be made available to users much faster, resulting in enhanced user experience. In this paper we propose an architecture for federated learning from personalized, graph based recommendations computed on client devices, collectively creating & enhancing a global knowledge graph. In this network, individual users will ‘train’ their local recommender engines, while a server-based voting mechanism aggregates the developing client-side models, preventing over-fitting on highly subjective data from tarnishing the global model.
Complete list of metadata

Cited literature [17 references]  Display  Hide  Download

https://hal.inria.fr/hal-01677145
Contributor : Hal Ifip <>
Submitted on : Monday, January 8, 2018 - 9:50:16 AM
Last modification on : Wednesday, March 28, 2018 - 4:35:04 PM
Long-term archiving on: : Friday, May 4, 2018 - 6:43:39 PM

File

456304_1_En_24_Chapter.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Bernd Malle, Nicola Giuliani, Peter Kieseberg, Andreas Holzinger. The More the Merrier - Federated Learning from Local Sphere Recommendations. 1st International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2017, Reggio, Italy. pp.367-373, ⟨10.1007/978-3-319-66808-6_24⟩. ⟨hal-01677145⟩

Share

Metrics

Record views

330

Files downloads

157