79 research outputs found
Centralized Coded Caching with User Cooperation
In this paper, we consider the coded-caching broadcast network with user
cooperation, where a server connects with multiple users and the users can
cooperate with each other through a cooperation network. We propose a
centralized coded caching scheme based on a new deterministic placement
strategy and a parallel delivery strategy. It is shown that the new scheme
optimally allocate the communication loads on the server and users, obtaining
cooperation gain and parallel gain that greatly reduces the transmission delay.
Furthermore, we show that the number of users who parallelly send information
should decrease when the users' caching size increases. In other words, letting
more users parallelly send information could be harmful. Finally, we derive a
constant multiplicative gap between the lower bound and upper bound on the
transmission delay, which proves that our scheme is order optimal.Comment: 9 pages, submitted to ITW201
Multi-access Coded Caching with Optimal Rate and Linear Subpacketization under PDA and Consecutive Cyclic Placement
This work considers the multi-access caching system proposed by Hachem et
al., where each user has access to L neighboring caches in a cyclic wrap-around
fashion. We first propose a placement strategy called the consecutive cyclic
placement, which achieves the maximal local caching gain. Then under the
consecutive cyclic placement, we derive the optimal coded caching gain from the
perspective of Placement Delivery Array (PDA), thus obtaining a lower bound on
the rate of PDA. Finally, under the consecutive cyclic placement, we construct
a class of PDA, leading to a multi-access coded caching scheme with linear
subpacketization, which achieves our derived lower bound for some parameters;
while for other parameters, the achieved coded caching gain is only 1 less than
the optimal one. Analytical and numerical comparisons of the proposed scheme
with existing schemes are provided to validate the performance.Comment: 30 pages, 7 figure
Hierarchical Cache-Aided Linear Function Retrieval with Security and Privacy Constraints
The hierarchical caching system where a server connects with multiple mirror
sites, each connecting with a distinct set of users, and both the mirror sites
and users are equipped with caching memories has been widely studied. However
all the existing works focus on single file retrieval, i.e., each user requests
one file, and ignore the security and privacy threats in communications. In
this paper we investigate the linear function retrieval problem for
hierarchical caching systems with content security and demand privacy, i.e.,
each user requests a linear combination of files, and meanwhile the files in
the library are protected against wiretappers and users' demands are kept
unknown to other users and unconnected mirror sites. First we propose a new
combination structure named hierarchical placement delivery array (HPDA), which
characterizes the data placement and delivery strategy of a coded caching
scheme. Then we construct two classes of HPDAs. Consequently two classes of
schemes with or without security and privacy are obtained respectively where
the first dedicates to minimizing the transmission load for the first hop and
can achieve the optimal transmission load for the first hop if ignoring the
security and privacy constraints; the second has more flexible parameters on
the memory sizes and a lower subpacketization compared with the first one, and
achieves a tradeoff between subpacketization and transmission loads.Comment: arXiv admin note: substantial text overlap with arXiv:2205.0023
DPFormer: Learning Differentially Private Transformer on Long-Tailed Data
The Transformer has emerged as a versatile and effective architecture with
broad applications. However, it still remains an open problem how to
efficiently train a Transformer model of high utility with differential privacy
guarantees. In this paper, we identify two key challenges in learning
differentially private Transformers, i.e., heavy computation overhead due to
per-sample gradient clipping and unintentional attention distraction within the
attention mechanism. In response, we propose DPFormer, equipped with Phantom
Clipping and Re-Attention Mechanism, to address these challenges. Our
theoretical analysis shows that DPFormer can reduce computational costs during
gradient clipping and effectively mitigate attention distraction (which could
obstruct the training process and lead to a significant performance drop,
especially in the presence of long-tailed data). Such analysis is further
corroborated by empirical results on two real-world datasets, demonstrating the
efficiency and effectiveness of the proposed DPFormer
One-Bit Byzantine-Tolerant Distributed Learning via Over-the-Air Computation
Distributed learning has become a promising computational parallelism
paradigm that enables a wide scope of intelligent applications from the
Internet of Things (IoT) to autonomous driving and the healthcare industry.
This paper studies distributed learning in wireless data center networks, which
contain a central edge server and multiple edge workers to collaboratively
train a shared global model and benefit from parallel computing. However, the
distributed nature causes the vulnerability of the learning process to faults
and adversarial attacks from Byzantine edge workers, as well as the severe
communication and computation overhead induced by the periodical information
exchange process. To achieve fast and reliable model aggregation in the
presence of Byzantine attacks, we develop a signed stochastic gradient descent
(SignSGD)-based Hierarchical Vote framework via over-the-air computation
(AirComp), where one voting process is performed locally at the wireless edge
by taking advantage of Bernoulli coding while the other is operated
over-the-air at the central edge server by utilizing the waveform superposition
property of the multiple-access channels. We comprehensively analyze the
proposed framework on the impacts including Byzantine attacks and the wireless
environment (channel fading and receiver noise), followed by characterizing the
convergence behavior under non-convex settings. Simulation results validate our
theoretical achievements and demonstrate the robustness of our proposed
framework in the presence of Byzantine attacks and receiver noise.Comment: This work has been submitted to the IEEE for possible publication.
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Coded Caching Schemes for Two-dimensional Caching-aided Ultra-Dense Networks
Coded caching technique is an efficient approach to reduce the transmission
load in networks and has been studied in heterogeneous network settings in
recent years. In this paper, we consider a new widespread caching system called
two-dimensional (2D) caching-aided ultra-dense network
(UDN) with a server containing files, cache nodes arranged neatly
on a grid with rows and columns, and cache-less users randomly
distributed around cache nodes. Each cache node can cache at most
files and has a certain service region by Euclidean distance. The server
connects to users through an error-free shared link and the users in the
service region of a cache node can freely retrieve all cached contents of this
cache node. We aim to design a coded caching scheme for 2D caching-aided UDN
systems to reduce the transmission load in the worst case while meeting all
possible users' demands. First, we divide all possible users into four classes
according to their geographical locations. Then our first order optimal scheme
is proposed based on the Maddah-Ali and Niesen scheme. Furthermore, by
compressing the transmitted signals of our first scheme based on Maximum
Distance Separable (MDS) code, we obtain an improved order optimal scheme with
a smaller transmission load.Comment: 44 page
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