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  1. Free, publicly-accessible full text available October 4, 2022
  2. Federated multi-armed bandits (FMAB) is a new bandit paradigm that parallels the federated learning (FL) framework in supervised learning. It is inspired by practical applications in cognitive radio and recommender systems, and enjoys features that are analogous to FL. This paper proposes a general framework of FMAB and then studies two specific federated bandit models. We first study the approximate model where the heterogeneous local models are random realizations of the global model from an unknown distribution. This model introduces a new uncertainty of client sampling, as the global model may not be reliably learned even if the finite localmore »models are perfectly known. Furthermore, this uncertainty cannot be quantified a priori without knowledge of the suboptimality gap. We solve the approximate model by proposing Federated Double UCB (Fed2-UCB), which constructs a novel “double UCB” principle accounting for uncertainties from both arm and client sampling. We show that gradually admitting new clients is critical in achieving an O(log(T)) regret while explicitly considering the communication loss. The exact model, where the global bandit model is the exact average of heterogeneous local models, is then studied as a special case. We show that, somewhat surprisingly, the order-optimal regret can be achieved independent of the number of clients with a careful choice of the update periodicity. Experiments using both synthetic and real-world datasets corroborate the theoretical analysis and demonstrate the effectiveness and efficiency of the proposed algorithms.« less
  3. Deep neural networks (DNNs) emerge as a key component in various applications. However, the ever-growing DNN size hinders efficient processing on hardware. To tackle this problem, on the algorithmic side, compressed DNN models are explored, of which block-circulant DNN models are memory efficient and hardware-friendly; on the hardware side, resistive random-access memory (ReRAM) based accelerators are promising for in-situ processing of DNNs. In this work, we design an accelerator named ReBoc for accelerating block-circulant DNNs in ReRAM to reap the benefits of light-weight models and efficient in-situ processing simultaneously. We propose a novel mapping scheme which utilizes Horizontal Weight Slicingmore »and Intra-Crossbar Weight Duplication to map block-circulant DNN models onto ReRAM crossbars with significant improved crossbar utilization. Moreover, two specific techniques, namely Input Slice Reusing and Input Tile Sharing are introduced to take advantage of the circulant calculation feature in block- circulant DNNs to reduce data access and buffer size. In REBOC, a DNN model is executed within an intra-layer processing pipeline and achieves respectively 96× and 8.86× power efficiency improvement compared to the state-of-the-art FPGA and ASIC accelerators for block-circulant neural networks. Compared to ReRAM-based DNN accelerators, REBOC achieves averagely 4.1× speedup and 2.6× energy reduction.« less
  4. The devastating impact of climate change on coral reefs has reinforced our need to better understand their causes, especially the ones related to humans. Simultaneously, we need to raise awareness about the significance of reefs, both as an ecological host to twenty-five percent of marine life and as a key economic resource for millions of people. Opportunities afforded through coral reef research coupled with advances in computational modeling platforms may provide a unique opportunity to introduce the study of corals into K-12 STEM curricula by combining computational thinking (CT) constructs to build computational models that allow students to explore andmore »systematically study the effects of climate change on the reefs. We outline such a computational modeling curriculum in this paper.« less