%0 Conference Proceedings
%T The More the Merrier - Federated Learning from Local Sphere Recommendations
%+ Medical University Graz
%+ SBA Research
%A Malle, Bernd
%A Giuliani, Nicola
%A Kieseberg, Peter
%A Holzinger, Andreas
%Z Part 6: MAKE Semantics
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 1st International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE)
%C Reggio, Italy
%Y Andreas Holzinger
%Y Peter Kieseberg
%Y A Min Tjoa
%Y Edgar Weippl
%I Springer International Publishing
%3 Machine Learning and Knowledge Extraction
%V LNCS-10410
%P 367-373
%8 2017-08-29
%D 2017
%R 10.1007/978-3-319-66808-6_24
%K Machine Learning
%K Federated ML
%K Interactive ML
%K The local sphere
%K Graph based personalized recommenders
%K Distributed bagging
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X 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.
%G English
%Z TC 5
%Z TC 8
%Z TC 12
%Z WG 8.4
%Z WG 8.9
%Z WG 12.9
%2 https://inria.hal.science/hal-01677145/document
%2 https://inria.hal.science/hal-01677145/file/456304_1_En_24_Chapter.pdf
%L hal-01677145
%U https://inria.hal.science/hal-01677145
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-TC8
%~ IFIP-WG8-4
%~ IFIP-WG8-9
%~ IFIP-LNCS-10410
%~ IFIP-CD-MAKE
%~ IFIP-WG12-9