76 research outputs found
TaiJi: Longest Chain Availability with BFT Fast Confirmation
Most state machine replication protocols are either based on the 40-years-old
Byzantine Fault Tolerance (BFT) theory or the more recent Nakamoto's longest
chain design. Longest chain protocols, designed originally in the Proof-of-Work
(PoW) setting, are available under dynamic participation, but has probabilistic
confirmation with long latency dependent on the security parameter. BFT
protocols, designed for the permissioned setting, has fast deterministic
confirmation, but assume a fixed number of nodes always online. We present a
new construction which combines a longest chain protocol and a BFT protocol to
get the best of both worlds. Using this construction, we design TaiJi, the
first dynamically available PoW protocol which has almost deterministic
confirmation with latency independent of the security parameter. In contrast to
previous hybrid approaches which use a single longest chain to sample
participants to run a BFT protocol, our native PoW construction uses many
independent longest chains to sample propose actions and vote actions for the
BFT protocol. This design enables TaiJi to inherit the full dynamic
availability of Bitcoin, as well as its full unpredictability, making it secure
against fully-adaptive adversaries with up to 50% of online hash power
Communication-Aware Computing for Edge Processing
We consider a mobile edge computing problem, in which mobile users offload
their computation tasks to computing nodes (e.g., base stations) at the network
edge. The edge nodes compute the requested functions and communicate the
computed results to the users via wireless links. For this problem, we propose
a Universal Coded Edge Computing (UCEC) scheme for linear functions to
simultaneously minimize the load of computation at the edge nodes, and maximize
the physical-layer communication efficiency towards the mobile users. In the
proposed UCEC scheme, edge nodes create coded inputs of the users, from which
they compute coded output results. Then, the edge nodes utilize the computed
coded results to create communication messages that zero-force all the
interference signals over the air at each user. Specifically, the proposed
scheme is universal since the coded computations performed at the edge nodes
are oblivious of the channel states during the communication process from the
edge nodes to the users.Comment: To Appear in ISIT 201
Information-Theoretically Private Matrix Multiplication From MDS-Coded Storage
We study two problems of private matrix multiplication, over a distributed
computing system consisting of a master node, and multiple servers who
collectively store a family of public matrices using Maximum-Distance-Separable
(MDS) codes. In the first problem of Private and Secure Matrix Multiplication
from Colluding servers (MDS-C-PSMM), the master intends to compute the product
of its confidential matrix with a target matrix stored on the
servers, without revealing any information about and the index of
target matrix to some colluding servers. In the second problem of Fully Private
Matrix Multiplication from Colluding servers (MDS-C-FPMM), the matrix
is also selected from another family of public matrices stored at
the servers in MDS form. In this case, the indices of the two target matrices
should both be kept private from colluding servers. We develop novel strategies
for MDS-C-PSMM and MDS-C-FPMM, which simultaneously guarantee
information-theoretic data/index privacy and computation correctness. The key
ingredient is a careful design of secret sharings of the matrix
and the private indices, which are tailored to matrix multiplication task and
MDS storage structure, such that the computation results from the servers can
be viewed as evaluations of a polynomial at distinct points, from which the
intended result can be obtained through polynomial interpolation. We compare
the proposed MDS-C-PSMM strategy with a previous MDS-PSMM strategy with a
weaker privacy guarantee (non-colluding servers), and demonstrate substantial
improvements over the previous strategy in terms of communication and
computation performance
Near-Optimal Straggler Mitigation for Distributed Gradient Methods
Modern learning algorithms use gradient descent updates to train inferential
models that best explain data. Scaling these approaches to massive data sizes
requires proper distributed gradient descent schemes where distributed worker
nodes compute partial gradients based on their partial and local data sets, and
send the results to a master node where all the computations are aggregated
into a full gradient and the learning model is updated. However, a major
performance bottleneck that arises is that some of the worker nodes may run
slow. These nodes a.k.a. stragglers can significantly slow down computation as
the slowest node may dictate the overall computational time. We propose a
distributed computing scheme, called Batched Coupon's Collector (BCC) to
alleviate the effect of stragglers in gradient methods. We prove that our BCC
scheme is robust to a near optimal number of random stragglers. We also
empirically demonstrate that our proposed BCC scheme reduces the run-time by up
to 85.4% over Amazon EC2 clusters when compared with other straggler mitigation
strategies. We also generalize the proposed BCC scheme to minimize the
completion time when implementing gradient descent-based algorithms over
heterogeneous worker nodes
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