Federated Learning (FL) is very appealing for its privacy benefits: essentially, a global model is trained with updates computed on mobile devices while keeping the data of users local. Standard FL infrastructures are however designed to have no energy or performance impact on mobile devices, and are therefore not suitable for applications that require frequent ( online ) model updates, such as news recommenders. This paper presents FLeet , the first Online FL system, acting as a middleware between the Android OS and the machine learning application. FLeet combines the privacy of Standard FL with the precision of online learning thanks to two core components: (i) I-Prof , a new lightweight profiler that predicts and controls the impact of learning tasks on mobile devices, and (ii) AdaSGD , a new adaptive learning algorithm that is resilient to delayed updates. Our extensive evaluation shows that Online FL, as implemented by FLeet , can deliver a 2.3 × quality boost compared to Standard FL, while only consuming 0.036% of the battery per day. I-Prof can accurately control the impact of learning tasks by improving the prediction accuracy by up to 3.6 × in terms of computation time, and by up to 19 × in terms of energy. AdaSGD outperforms alternative FL approaches by 18.4% in terms of convergence speed on heterogeneous data.
Similarity computations are crucial in various web activities like advertisements, search or trust-distrust predictions. These similarities often vary with time as product perception and popularity constantly change with users' evolving inclination. The huge volume of user-generated data typically results in heavyweight computations for even a single similarity update. We present I-SIM, a novel similarity metric that enables lightweight similarity computations in an incremental and temporal manner. Incrementality enables updates with low latency whereas temporality captures users' evolving inclination. The main idea behind I-SIM is to disintegrate the similarity metric into mutually independent time-aware factors which can be updated incrementally. We illustrate the efficacy of I-SIM through a novel recommender (SWIFT) as well as through a trust-distrust predictor in Online Social Networks (I-TRUST). We experimentally show that I-SIM enables fast and accurate predictions in an energy-efficient manner.
In this paper we tackle the challenge of making the stochastic coordinate descent algorithm differentially private. Compared to the classical gradient descent algorithm where updates operate on a single model vector and controlled noise addition to this vector suffices to hide critical information about individuals, stochastic coordinate descent crucially relies on keeping auxiliary information in memory during training. This auxiliary information provides an additional privacy leak and poses the major challenge addressed in this work. Driven by the insight that under independent noise addition, the consistency of the auxiliary information holds in expectation, we present DP-SCD, the first differentially private stochastic coordinate descent algorithm. We give a convergence analysis of our new method, analyze its privacy-utility trade-off and demonstrate competitive performance against the popular stochastic gradient descent alternative while requiring significantly less tuning.
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