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This article presents the design, implementation, and evaluation of FLSys, a mobile-cloud federated learning (FL) system, which can be a key component for an open ecosystem of FL models and apps. FLSys is designed to work on smart phones with mobile sensing data. It balances model performance with resource consumption, tolerates communication failures, and achieves scalability. In FLSys, different DL models with different FL aggregation methods can be trained and accessed concurrently by different apps. Furthermore, FLSys provides advanced privacy preserving mechanisms and a common API for third-party app developers to access FL models. FLSys adopts a modular design and is implemented in Android and AWS cloud. We co-designed FLSys with a human activity recognition (HAR) model. HAR sensing data was collected in the wild from 100+ college students during a 4-month period. We implemented HAR-Wild, a CNN model tailored to mobile devices, with a data augmentation mechanism to mitigate the problem of non-Independent and Identically Distributed data. A sentiment analysis model is also used to demonstrate that FLSys effectively supports concurrent models. This article reports our experience and lessons learned from conducting extensive experiments using simulations, Android/Linux emulations, and Android phones that demonstrate FLSys achieves good model utility and practical system performance.

The ability to summarize a large number of network patterns discovered from biomedical data provides valuable information for use in many applications. We show that several variants of the problem are all NP-hard, and merging network patterns is a practical solution for these applications. In this work, we propose an algorithmic framework for merging network patterns. We have developed fast algorithms under this general framework which supports several types of biomedical network data. In addition, our empirical study demonstrates that our algorithms are efficient in merging a large number of biomedical network patterns and can be configured for various knowledge discovery purposes.The ability to summarize a large number of network patterns discovered from biomedical data provides valuable information for use in many applications. We show that several variants of the problem are all NP-hard, and merging network patterns is a practical solution for these applications. In this work, we propose an algorithmic framework for merging network patterns. We have developed fast algorithms under this general framework which supports several types of biomedical network data. In addition, our empirical study demonstrates that our algorithms are efficient in merging a large number of biomedical network patterns and can be configured for various knowledge discovery purposes.

In this report, we summarize the research issues, contents, and outcomes of the two recent workshops on Mining Multiple Information Sources (MMIS-07, 08) collocated with the 13th and the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-07 and KDD-08). We first summarize the research issues and topics which in workshop co-chairs' view are the major challenges for mining multiple information sources. Then we briefiy introduce the content of the contributed papers in two years' program, along with the introduction to three keynote talks given by the invited speakers.

In this paper, we revisit the problem of performance prediction on shared memory parallel machines, motivated by the need for selecting parallelization strategy for random write reductions. Such reductions frequently arise in data mining algorithms.In our previous work, we have developed a number of techniques for parallelizing this class of reductions. Our previous work has shown that each of the three techniques, full replication, optimized full locking, and cache-sensitive, can outperform others depending upon problem, dataset, and machine parameters. Therefore, an important question is, "Can we predict the performance of these techniques for a given problem, dataset, and machine?".This paper addresses this question by developing an analytical performance model that captures a two-level cache, coherence cache misses, TLB misses, locking overheads, and contention for memory. Analytical model is combined with results from micro-benchmarking to predict performance on real machines. We have validated our model on two different SMP machines. Our results show that our model effectively captures the impact of memory hierarchy (two-level cache and TLB) as well as the factors that limit parallelism (contention for locks, memory contention, and coherence cache misses). The difference between predicted and measured performance is within 20% in almost all cases. Moreover, the model is quite accurate in predicting the relative performance of the three parallelization techniques.