456 research outputs found
Auction-Based Distributed Resource Allocation for Cooperation Transmission in Wireless Networks
Cooperative transmission can greatly improve communication system performance
by taking advantage of the broadcast nature of wireless channels. Most previous
work on resource allocation for cooperation transmission is based on
centralized control. In this paper, we propose two share auction mechanisms,
the SNR auction and the power auction, to distributively coordinate the
resource allocation among users. We prove the existence, uniqueness and
effectiveness of the auction results. In particular, the SNR auction leads to a
fair resource allocation among users, and the power auction achieves a solution
that is close to the efficient allocation.Comment: To appear in the Proceedings of the IEEE IEEE Global Communications
Conference (GLOBECOM), Washington, DC, November 26 - 30, 200
Auction-based Resource Allocation for Multi-relay Asynchronous Cooperative Networks
Resource allocation is considered for cooperative transmissions in
multiple-relay wireless networks. Two auction mechanisms, SNR auctions and
power auctions, are proposed to distributively coordinate the allocation of
power among multiple relays. In the SNR auction, a user chooses the relay with
the lowest weighted price. In the power auction, a user may choose to use
multiple relays simultaneously, depending on the network topology and the
relays' prices. Sufficient conditions for the existence (in both auctions) and
uniqueness (in the SNR auction) of the Nash equilibrium are given. The fairness
of the SNR auction and efficiency of the power auction are further discussed.
It is also proven that users can achieve the unique Nash equilibrium
distributively via best response updates in a completely asynchronous manner.Comment: To appear in the Proceedings of the 2008 IEEE International
Conference on Acoustics, Speech and Signal Processing, Las Vegas, NV, March
30 to April 4, 200
FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning
Knowledge distillation (KD) can enable collaborative learning among
distributed clients that have different model architectures and do not share
their local data and model parameters with others. Each client updates its
local model using the average model output/feature of all client models as the
target, known as federated KD. However, existing federated KD methods often do
not perform well when clients' local models are trained with heterogeneous
local datasets. In this paper, we propose Federated knowledge distillation
enabled by Adversarial Learning (FedAL) to address the data heterogeneity among
clients. First, to alleviate the local model output divergence across clients
caused by data heterogeneity, the server acts as a discriminator to guide
clients' local model training to achieve consensus model outputs among clients
through a min-max game between clients and the discriminator. Moreover,
catastrophic forgetting may happen during the clients' local training and
global knowledge transfer due to clients' heterogeneous local data. Towards
this challenge, we design the less-forgetting regularization for both local
training and global knowledge transfer to guarantee clients' ability to
transfer/learn knowledge to/from others. Experimental results show that FedAL
and its variants achieve higher accuracy than other federated KD baselines
Incentive Mechanism Design for Distributed Ensemble Learning
Distributed ensemble learning (DEL) involves training multiple models at
distributed learners, and then combining their predictions to improve
performance. Existing related studies focus on DEL algorithm design and
optimization but ignore the important issue of incentives, without which
self-interested learners may be unwilling to participate in DEL. We aim to fill
this gap by presenting a first study on the incentive mechanism design for DEL.
Our proposed mechanism specifies both the amount of training data and reward
for learners with heterogeneous computation and communication costs. One design
challenge is to have an accurate understanding regarding how learners'
diversity (in terms of training data) affects the ensemble accuracy. To this
end, we decompose the ensemble accuracy into a diversity-precision tradeoff to
guide the mechanism design. Another challenge is that the mechanism design
involves solving a mixed-integer program with a large search space. To this
end, we propose an alternating algorithm that iteratively updates each
learner's training data size and reward. We prove that under mild conditions,
the algorithm converges. Numerical results using MNIST dataset show an
interesting result: our proposed mechanism may prefer a lower level of learner
diversity to achieve a higher ensemble accuracy.Comment: Accepted to IEEE GLOBECOM 202
Theoretical Investigation of the Black-body Zeeman Shift for Microwave Atomic Clocks
With the development of microwave atomic clocks, the Zeeman shifts for the
spectral lines of black-body radiation need to be investigated carefully. In
this Letter, the frequency shifts of hyperfine splittings of atomic ground
states due to the magnetic field of black-body radiation are reported. The
relative frequency shifts of different alkali atoms and alkali-like ions, which
could be candidates of microwave atomic clocks, were calculated. The results
vary from to
for different atoms considered. These
results are consistent with previous work but with greater precision, detailed
derivations, and a clear physical picture
Characterizing the IRC-based Botnet Phenomenon
Botnets, networks of compromised machines that can be remotely controlled by an attacker, are one of the most common attack platforms nowadays. They can, for example, be used to launch distributed denial-of-service (DDoS) attacks, steal sensitive information, or send spam emails. A long-term measurement study of botnet activities is useful as a basis for further research on global botnet mitigation and disruption techniques. We have built a distributed and fully-automated botnet measurement system which allows us to collect data on the botnet activity we observe in China. Based on the analysis of tracking records of 3,290 IRC-based botnets during a period of almost twelve months, this paper presents several novel results of botnet activities which can only be measured via long-term measurements. These include. amongst others, botnet lifetime, botnet discovery trends and distributions, command and control channel distributions, botnet size and end-host distributions. Furthermore, our measurements confirm and extend several previous results from this area. Our results show that the botnet problem is of global scale, with a scattered distribution of the control infrastructure and also a scattered distribution of the victims. Furthermore, the control infrastructure itself is rather flexible, with an average lifetime of a Command \& Control server of about 54 days. These results can also leverage research in the area of botnet detection, mitigation, and disruption: only by understanding the problem in detail, we can develop efficient counter measures
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