Options
Geraci, James
Preferred name
Geraci, James
Official Name
Geraci, James
Research Output
Now showing 1 - 2 of 2
- PublicationFedFast: Going Beyond Average for Faster Training of Federated Recommender Systems(ACM, 2020-08-27)
; ; ; ; ; ; ; Federated learning (FL) is quickly becoming the de facto standard for the distributed training of deep recommendation models, us-ing on-device user data and reducing server costs. In a typical FLprocess, a central server tasks end-users to train a shared recommen-dation model using their local data. The local models are trained over several rounds on the users’ devices and the server combinesthem into a global model, which is sent to the devices for the pur-pose of providing recommendations. Standard FL approaches userandomly selected users for training at each round, and simply average their local models to compute the global model. The resulting federated recommendation models require significant client effortto train and many communication rounds before they converge to asatisfactory accuracy. Users are left with poor quality recommendations until the late stages of training. We present a novel technique, FedFast, to accelerate distributed learning which achieves goodaccuracy for all users very early in the training process. We achievethis by sampling from a diverse set of participating clients in each training round and applying an active aggregation method that propagates the updated model to the other clients. Consequently, with FedFast the users benefit from far lower communication costsand more accurate models that can be consumed anytime during the training process even at the very early stages. We demonstrate the efficacy of our approach across a variety of benchmark datasetsand in comparison to state-of-the-art recommendation techniques569Scopus© Citations 108 - PublicationPDMFRec: A Decentralised Matrix Factorisation with Tunable User-centric Privacy(ACM, 2019-09-19)
; ; ; ; ; ; ; Conventional approaches to matrix factorisation (MF) typically rely on a centralised collection of user data for building a MF model. This approach introduces an increased risk when it comes to user privacy. In this short paper we propose an alternative, user-centric, privacy enhanced, decentralised approach to MF. Our method pushes the computation of the recommendation model to the user’s device, and eliminates the need to exchange sensitive personal information; instead only the loss gradients of local (device-based) MF models need to be shared. Moreover, users can select the amount and type of information to be shared, for enhanced privacy. We demonstrate the effectiveness of this approach by considering different levels of user privacy in comparison with state-of-the-art alternatives.488Scopus© Citations 27