Flow: Per-Instance Personalized Federated Learning Through Dynamic Routing

Personalization in Federated Learning (FL) aims to modify a collaboratively trained global model according to each client. Current approaches to personalization in FL are at a coarse granularity, i.e. all the input instances of a client use the same personalized model. This ignores the fact that some instances are more accurately handled by the global model due to better generalizability. To address this challenge, this work proposes Flow, a fine-grained stateless personalized FL approach. Flow creates dynamic personalized models by learning a routing mechanism that determines whether an input instance prefers the local parameters or its global counterpart. Thus, Flow introduces per-instance routing in addition to leveraging per-client personalization to improve accuracies at each client. Further, Flow is stateless which makes it unnecessary for a client to retain its personalized state across FL rounds. This makes Flow practical for large-scale FL settings and friendly to newly joined clients. Evaluations on Stackoverflow, Reddit, and EMNIST datasets demonstrate the superiority in prediction accuracy of Flow over state-of-the-art non-personalized and only per-client personalized approaches to FL

Correlated Stochastic Knapsack with a Submodular Objective

We study the correlated stochastic knapsack problem of a submodular target function, with optional additional constraints. We utilize the multilinear extension of submodular function, and bundle it with an adaptation of the relaxed linear constraints from Ma [Mathematics of Operations Research, Volume 43(3), 2018] on correlated stochastic knapsack problem. The relaxation is then solved by the stochastic continuous greedy algorithm, and rounded by a novel method to fit the contention resolution scheme (Feldman et al. [FOCS 2011]). We obtain a pseudo-polynomial time (1 - 1/?e)/2 ? 0.1967 approximation algorithm with or without those additional constraints, eliminating the need of a key assumption and improving on the (1 - 1/?e)/2 ? 0.1106 approximation by Fukunaga et al. [AAAI 2019]

Underwater Data Collection Using Robotic Sensor Networks

We examine the problem of utilizing an autonomous underwater vehicle (AUV) to collect data from an underwater sensor network. The sensors in the network are equipped with acoustic modems that provide noisy, range-limited communication. The AUV must plan a path that maximizes the information collected while minimizing travel time or fuel expenditure. We propose AUV path planning methods that extend algorithms for variants of the Traveling Salesperson Problem (TSP). While executing a path, the AUV can improve performance by communicating with multiple nodes in the network at once. Such multi-node communication requires a scheduling protocol that is robust to channel variations and interference. To this end, we examine two multiple access protocols for the underwater data collection scenario, one based on deterministic access and another based on random access. We compare the proposed algorithms to baseline strategies through simulated experiments that utilize models derived from experimental test data. Our results demonstrate that properly designed communication models and scheduling protocols are essential for choosing the appropriate path planning algorithms for data collection.United States. Office of Naval Research (ONR N00014-09-1-0700)United States. Office of Naval Research (ONR N00014-07-1-00738)National Science Foundation (U.S.) (NSF 0831728)National Science Foundation (U.S.) (NSF CCR-0120778)National Science Foundation (U.S.) (NSF CNS-1035866

Communication protocols for underwater data collection using a robotic sensor network

We examine the problem of collecting data from an underwater sensor network using an autonomous underwater vehicle (AUV). The sensors in the network are equipped with acoustic modems that provide noisy, range-limited communication to the AUV. One challenge in this scenario is to plan paths that maximize the information collected and minimize travel time. While executing a path, the AUV can improve performance by communicating with multiple nodes in the network at once. Such multi-node communication requires a scheduling protocol that is robust to channel variations and interference. To solve this problem, we develop and test a multiple access control protocol for the underwater data collection scenario. We perform simulated experiments that utilize a realistic model of acoustic communication taken from experimental test data. These simulations demonstrate that properly designed scheduling protocols are essential for choosing the appropriate path planning algorithms for data collection.United States. Office of Naval Research (Grant N00014-09-1-070)United States. Office of Naval Research (Grant N00014-07-1-00738)National Science Foundation (U.S.) (Grant 0831728)National Science Foundation (U.S.) (Grant CCR-0120778)National Science Foundation (U.S.) (Grant CNS-1035866

An Algorithm for Bit-Serial Addition of SPT Numbers for Multiplierless Realization of Adaptive Equalizers

The "sum of power of two (SPT)" is an effective format to represent multipliers in a digital filter which reduces the complexity of multiplication to a few shift and add operations. The canonic SPT is a special sparse SPT representation that guarantees occurrence of at least one zero between every two nonzero SPT bits. This paper presents a novel algorithm for bit serial addition of two numbers, each given in canonic SPT form, to produce a result also in canonic SPT. The proposed algorithm uses the properties of canonic SPT numbers effectively, resulting in considerable reduction of the hardware complexity of the bit serial adder. The algorithm is particularly useful for multiplier free realization of adaptive filters and equalizers, where the current weight vector and the update term, both presumed to be given in canonic SPT, are required to be added in a way that retains the canonic SPT format for the updated weight vector.APSIPA ASC 2009: Asia-Pacific Signal and Information Processing Association, 2009 Annual Summit and Conference. 4-7 October 2009. Sapporo, Japan. Poster session: Signal Processing Theory and Methods I (6 October 2009)

Bilinear Map

� Identifiability crucial in inverse problems � Not well understood for non-linear systems/constraints � We develop theory for Bilinear Inverse Problems � subsumes blind estimation � deterministic characterization of identifiability � probabilistic scaling law � general conic constraints included, e.g. sparsity and low rank constraints � Connect blind estimation to low-rank matrix recover