242 research outputs found
Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments
Decentralized systems are a subset of distributed systems where multiple
authorities control different components and no authority is fully trusted by
all. This implies that any component in a decentralized system is potentially
adversarial. We revise fifteen years of research on decentralization and
privacy, and provide an overview of key systems, as well as key insights for
designers of future systems. We show that decentralized designs can enhance
privacy, integrity, and availability but also require careful trade-offs in
terms of system complexity, properties provided, and degree of
decentralization. These trade-offs need to be understood and navigated by
designers. We argue that a combination of insights from cryptography,
distributed systems, and mechanism design, aligned with the development of
adequate incentives, are necessary to build scalable and successful
privacy-preserving decentralized systems
Heterogeneous Federated Learning: State-of-the-art and Research Challenges
Federated learning (FL) has drawn increasing attention owing to its potential
use in large-scale industrial applications. Existing federated learning works
mainly focus on model homogeneous settings. However, practical federated
learning typically faces the heterogeneity of data distributions, model
architectures, network environments, and hardware devices among participant
clients. Heterogeneous Federated Learning (HFL) is much more challenging, and
corresponding solutions are diverse and complex. Therefore, a systematic survey
on this topic about the research challenges and state-of-the-art is essential.
In this survey, we firstly summarize the various research challenges in HFL
from five aspects: statistical heterogeneity, model heterogeneity,
communication heterogeneity, device heterogeneity, and additional challenges.
In addition, recent advances in HFL are reviewed and a new taxonomy of existing
HFL methods is proposed with an in-depth analysis of their pros and cons. We
classify existing methods from three different levels according to the HFL
procedure: data-level, model-level, and server-level. Finally, several critical
and promising future research directions in HFL are discussed, which may
facilitate further developments in this field. A periodically updated
collection on HFL is available at https://github.com/marswhu/HFL_Survey.Comment: 42 pages, 11 figures, and 4 table
REPUTATION COMPUTATION IN SOCIAL NETWORKS AND ITS APPLICATIONS
This thesis focuses on a quantification of reputation and presents models which compute reputation within networked environments. Reputation manifests past behaviors of users and helps others to predict behaviors of users and therefore reduce risks in future interactions. There are two approaches in computing reputation on networks- namely, the macro-level approach and the micro-level approach. A macro-level assumes that there exists a computing entity outside of a given network who can observe the entire network including degree distributions and relationships among nodes. In a micro-level approach, the entity is one of the nodes in a network and therefore can only observe the information local to itself, such as its own neighbors behaviors. In particular, we study reputation computation algorithms in online distributed environments such as social networks and develop reputation computation algorithms to address limitations of existing models. We analyze and discuss some properties of reputation values of a large number of agents including power-law distribution and their diffusion property. Computing reputation of another within a network requires knowledge of degrees of its neighbors. We develop an algorithm for estimating degrees of each neighbor. The algorithm considers observations associated with neighbors as a Bernoulli trial and repeatedly estimate degrees of neighbors as a new observation occurs. We experimentally show that the algorithm can compute the degrees of neighbors more accurately than a simple counting of observations. Finally, we design a bayesian reputation game where reputation is used as payoffs. The game theoretic view of reputation computation reflects another level of reality in which all agents are rational in sharing reputation information of others. An interesting behavior of agents within such a game theoretic environment is that cooperation- i.e., sharing true reputation information- emerges without an explicit punishment mechanism nor a direct reward mechanisms
Incentive-driven QoS in peer-to-peer overlays
A well known problem in peer-to-peer overlays is that no single entity has control over the software,
hardware and configuration of peers. Thus, each peer can selfishly adapt its behaviour to maximise its
benefit from the overlay. This thesis is concerned with the modelling and design of incentive mechanisms
for QoS-overlays: resource allocation protocols that provide strategic peers with participation incentives,
while at the same time optimising the performance of the peer-to-peer distribution overlay.
The contributions of this thesis are as follows. First, we present PledgeRoute, a novel contribution
accounting system that can be used, along with a set of reciprocity policies, as an incentive mechanism
to encourage peers to contribute resources even when users are not actively consuming overlay services.
This mechanism uses a decentralised credit network, is resilient to sybil attacks, and allows peers to
achieve time and space deferred contribution reciprocity. Then, we present a novel, QoS-aware resource
allocation model based on Vickrey auctions that uses PledgeRoute as a substrate. It acts as an incentive
mechanism by providing efficient overlay construction, while at the same time allocating increasing
service quality to those peers that contribute more to the network. The model is then applied to lagsensitive
chunk swarming, and some of its properties are explored for different peer delay distributions.
When considering QoS overlays deployed over the best-effort Internet, the quality received by a
client cannot be adjudicated completely to either its serving peer or the intervening network between
them. By drawing parallels between this situation and well-known hidden action situations in microeconomics,
we propose a novel scheme to ensure adherence to advertised QoS levels. We then apply
it to delay-sensitive chunk distribution overlays and present the optimal contract payments required,
along with a method for QoS contract enforcement through reciprocative strategies. We also present a
probabilistic model for application-layer delay as a function of the prevailing network conditions.
Finally, we address the incentives of managed overlays, and the prediction of their behaviour. We
propose two novel models of multihoming managed overlay incentives in which overlays can freely
allocate their traffic flows between different ISPs. One is obtained by optimising an overlay utility
function with desired properties, while the other is designed for data-driven least-squares fitting of the
cross elasticity of demand. This last model is then used to solve for ISP profit maximisation
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