20 research outputs found
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A normative approach to multi-agent systems for intelligent buildings
Building Management Systems (BMS) are widely adopted in modern buildings around the world in order to
provide high-quality building services, and reduce the running cost of the building. However, most BMS are
functionality-oriented and do not consider user personalization. The aim of this research is to capture and
represent building management rules using organizational semiotics methods. We implement Semantic
Analysis, which determines semantic units in building management and their relationship patterns of
behaviour, and Norm Analysis, which extracts and specifies the norms that establish how and when these
management actions occur. Finally, we propose a multi-agent framework for norm based building
management. This framework contributes to the design domain of intelligent building management system
by defining a set of behaviour patterns, and the norms that govern the real-time behaviour in a building
LLMs Can Understand Encrypted Prompt: Towards Privacy-Computing Friendly Transformers
Prior works have attempted to build private inference frameworks for
transformer-based large language models (LLMs) in a server-client setting,
where the server holds the model parameters and the client inputs the private
data for inference. However, these frameworks impose significant overhead when
the private inputs are forward propagated through the original LLMs. In this
paper, we show that substituting the computation- and communication-heavy
operators in the transformer architecture with privacy-computing friendly
approximations can greatly reduce the private inference costs with minor impact
on model performance. Compared to the state-of-the-art Iron (NeurIPS 2022), our
privacy-computing friendly model inference pipeline achieves a
acceleration in computation and an 80\% reduction in communication overhead,
while retaining nearly identical accuracy
Minimal deployable endpoint-driven network forwarding: principle, designs and applications
Networked systems now have significant impact on human lives: the Internet, connecting the world globally, is the foundation of our information age, the data centers, running hundreds of thousands of servers, drive the era of cloud computing, and even the Tor project, a networked system providing online anonymity, now serves millions of daily users.
Guided by the end-to-end principle, many computer networks have been designed with a simple and flexible core offering general data transfer service, whereas the bulk of the application-level functionalities have been implemented on endpoints that are attached to the edge of the network. Although the end-to-end design principle gives these networked systems tremendous success, a number of new requirements have emerged for computer networks and their running applications, including untrustworthy of endpoints, privacy requirement of endpoints, more demanding applications, the rise of third-party Intermediaries and the asymmetric capability of endpoints and so on. These emerging requirements have created various challenges in different networked systems.
To address these challenges, there are no obvious solutions without adding in-network functions to the network core. However, no design principle has ever been proposed for guiding the implementation of in-network functions. In this thesis, We propose the first such principle and apply this principle to propose four designs in three different networked systems to address four separate challenges. We demonstrate through detailed implementation and extensive evaluations that the proposed principle can live in harmony with the end-to-end principle, and a combination of the two principle offers more complete, effective and accurate guides for innovating the modern computer networks and their applications.Ope
FlowPolice: enforcing congestion accountability to defend against DDoS attacks
Defending the Internet against distributed denial of service (DDoS) attacks is a fundamental problem. Despite over a decade of research, little progress has been made on the real-world deployment of proposed approaches due to the prohibitive deployment hurdles. This thesis presents FlowPolice, a new DDoS defense mechanism capable of thwarting millions of attack flows, while requiring very lightweight deployment. Specifically, FlowPolice can immediately benefit the first deployed autonomous system (AS) without further deployment at other ASs, and a single deployed router can protect all downstream links that implement a simple prioritization mechanism. The design of FlowPolice suppresses attack traffic by forcing attackers to be accountable for congestion via proper rate limiting. To learn users’ congestion accountability, FlowPolice leverages a capability feedback mechanism so that the deploying router can make rate limiting decisions based only on its self-generated capability tags.
We use theoretical analysis, large scale simulation and Linux
implementation to demonstrate the effectiveness of FlowPolice. Specifically, the the- oretical analysis proves that FlowPolice ensures per-flow fair share at the bottleneck link. Our implementation shows that FlowPolice can scale up to handle very large scale DDoS attacks and introduces little packet process- ing overhead. We also perform detailed packet-level simulation to show that FlowPolice is effective to mitigate DDoS attacks
martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecture
The development of machine learning models requires a large amount of
training data. Data marketplaces are essential for trading high-quality,
private-domain data not publicly available online. However, due to growing data
privacy concerns, direct data exchange is inappropriate. Federated Learning
(FL) is a distributed machine learning paradigm that exchanges data utilities
(in form of local models or gradients) among multiple parties without directly
sharing the raw data. However, several challenges exist when applying existing
FL architectures to construct a data marketplace: (i) In existing FL
architectures, Data Acquirers (DAs) cannot privately evaluate local models from
Data Providers (DPs) prior to trading; (ii) Model aggregation protocols in
existing FL designs struggle to exclude malicious DPs without "overfitting" to
the DA's (possibly biased) root dataset; (iii) Prior FL designs lack a proper
billing mechanism to enforce the DA to fairly allocate the reward according to
contributions made by different DPs. To address above challenges, we propose
martFL, the first federated learning architecture that is specifically designed
to enable a secure utility-driven data marketplace. At a high level, martFL is
powered by two innovative designs: (i) a quality-aware model aggregation
protocol that achieves robust local model aggregation even when the DA's root
dataset is biased; (ii) a verifiable data transaction protocol that enables the
DA to prove, both succinctly and in zero-knowledge, that it has faithfully
aggregates the local models submitted by different DPs according to the
committed aggregation weights, based on which the DPs can unambiguously claim
the corresponding reward. We implement a prototype of martFL and evaluate it
extensively over various tasks. The results show that martFL can improve the
model accuracy by up to 25% while saving up to 64% data acquisition cost
Enabling Work-conserving Bandwidth Guarantees for Multi-tenant Datacenters via Dynamic Tenant-Queue Binding
Today's cloud networks are shared among many tenants. Bandwidth guarantees
and work conservation are two key properties to ensure predictable performance
for tenant applications and high network utilization for providers. Despite
significant efforts, very little prior work can really achieve both properties
simultaneously even some of them claimed so.
In this paper, we present QShare, an in-network based solution to achieve
bandwidth guarantees and work conservation simultaneously. QShare leverages
weighted fair queuing on commodity switches to slice network bandwidth for
tenants, and solves the challenge of queue scarcity through balanced tenant
placement and dynamic tenant-queue binding. QShare is readily implementable
with existing switching chips. We have implemented a QShare prototype and
evaluated it via both testbed experiments and simulations. Our results show
that QShare ensures bandwidth guarantees while driving network utilization to
over 91% even under unpredictable traffic demands.Comment: The initial work is published in IEEE INFOCOM 201
ResLT: Residual Learning for Long-tailed Recognition
Deep learning algorithms face great challenges with long-tailed data
distribution which, however, is quite a common case in real-world scenarios.
Previous methods tackle the problem from either the aspect of input space
(re-sampling classes with different frequencies) or loss space (re-weighting
classes with different weights), suffering from heavy over-fitting to tail
classes or hard optimization during training. To alleviate these issues, we
propose a more fundamental perspective for long-tailed recognition, {i.e., from
the aspect of parameter space, and aims to preserve specific capacity for
classes with low frequencies. From this perspective, the trivial solution
utilizes different branches for the head, medium, tail classes respectively,
and then sums their outputs as the final results is not feasible. Instead, we
design the effective residual fusion mechanism -- with one main branch
optimized to recognize images from all classes, another two residual branches
are gradually fused and optimized to enhance images from medium+tail classes
and tail classes respectively. Then the branches are aggregated into final
results by additive shortcuts. We test our method on several benchmarks, {i.e.,
long-tailed version of CIFAR-10, CIFAR-100, Places, ImageNet, and iNaturalist
2018. Experimental results manifest that our method achieves new
state-of-the-art for long-tailed recognition. Code will be available at
\url{https://github.com/FPNAS/ResLT}
Generalized Parametric Contrastive Learning
In this paper, we propose the Generalized Parametric Contrastive Learning
(GPaCo/PaCo) which works well on both imbalanced and balanced data. Based on
theoretical analysis, we observe that supervised contrastive loss tends to bias
high-frequency classes and thus increases the difficulty of imbalanced
learning. We introduce a set of parametric class-wise learnable centers to
rebalance from an optimization perspective. Further, we analyze our GPaCo/PaCo
loss under a balanced setting. Our analysis demonstrates that GPaCo/PaCo can
adaptively enhance the intensity of pushing samples of the same class close as
more samples are pulled together with their corresponding centers and benefit
hard example learning. Experiments on long-tailed benchmarks manifest the new
state-of-the-art for long-tailed recognition. On full ImageNet, models from
CNNs to vision transformers trained with GPaCo loss show better generalization
performance and stronger robustness compared with MAE models. Moreover, GPaCo
can be applied to the semantic segmentation task and obvious improvements are
observed on the 4 most popular benchmarks. Our code is available at
https://github.com/dvlab-research/Parametric-Contrastive-Learning.Comment: TPAMI 2023. arXiv admin note: substantial text overlap with
arXiv:2107.1202