2 research outputs found
Byzantine Fault Tolerant Distributed Linear Regression
This paper considers the problem of Byzantine fault tolerance in distributed
linear regression in a multi-agent system. However, the proposed algorithms are
given for a more general class of distributed optimization problems, of which
distributed linear regression is a special case. The system comprises of a
server and multiple agents, where each agent is holding a certain number of
data points and responses that satisfy a linear relationship (could be noisy).
The objective of the server is to determine this relationship, given that some
of the agents in the system (up to a known number) are Byzantine faulty (aka.
actively adversarial). We show that the server can achieve this objective, in a
deterministic manner, by robustifying the original distributed gradient descent
method using norm based filters, namely 'norm filtering' and 'norm-cap
filtering', incurring an additional log-linear computation cost in each
iteration. The proposed algorithms improve upon the existing methods on three
levels: i) no assumptions are required on the probability distribution of data
points, ii) system can be partially asynchronous, and iii) the computational
overhead (in order to handle Byzantine faulty agents) is log-linear in number
of agents and linear in dimension of data points. The proposed algorithms
differ from each other in the assumptions made for their correctness, and the
gradient filter they use.Comment: Manuscript revised by adding; a new improved filtering technique, and
convergence analysis with nois
Appliance-level Flexible Scheduling for Socio-technical Smart Grid Optimisation
Participation in residential energy demand response programs requires an
active role by the consumers. They contribute flexibility in how they use their
appliances as the means to adjust energy consumption, and reduce demand peaks,
possibly at the expense of their own comfort (e.g., thermal). Understanding the
collective potential of appliance-level flexibility for reducing demand peaks
is challenging and complex. For instance, physical characteristics of
appliances, usage preferences, and comfort requirements all influence consumer
flexibility, adoption, and effectiveness of demand response programs. To
capture and study such socio-technical factors and trade-offs, this paper
contributes a novel appliance-level flexible scheduling framework based on
consumers' self-determined flexibility and comfort requirements. By utilizing
this framework, this paper studies (i) consumers usage preferences across
various appliances, as well as their voluntary contribution of flexibility and
willingness to sacrifice comfort for improving grid stability, (ii) impact of
individual appliances on the collective goal of reducing demand peaks, and
(iii) the effect of variable levels of flexibility, cooperation, and
participation on the outcome of coordinated appliance scheduling. Experimental
evaluation using a novel dataset collected via a smartphone app shows that
higher consumer flexibility can significantly reduce demand peaks, with the
oven having the highest system-wide potential for this. Overall, the
cooperative approach allows for higher peak-shaving compared to non-cooperative
schemes that focus entirely on the efficiency of individual appliances. The
findings of this study can be used to design more cost-effective and granular
(appliance-level) demand response programs in participatory and decentralized
Smart Grids