38,755 research outputs found
Federated Online and Bandit Convex Optimization
We study the problems of distributed online and bandit convex optimization
against an adaptive adversary. We aim to minimize the average regret on
machines working in parallel over rounds with intermittent
communications. Assuming the underlying cost functions are convex and can be
generated adaptively, our results show that collaboration is not beneficial
when the machines have access to the first-order gradient information at the
queried points. This is in contrast to the case for stochastic functions, where
each machine samples the cost functions from a fixed distribution. Furthermore,
we delve into the more challenging setting of federated online optimization
with bandit (zeroth-order) feedback, where the machines can only access values
of the cost functions at the queried points. The key finding here is
identifying the high-dimensional regime where collaboration is beneficial and
may even lead to a linear speedup in the number of machines. We further
illustrate our findings through federated adversarial linear bandits by
developing novel distributed single and two-point feedback algorithms. Our work
is the first attempt towards a systematic understanding of federated online
optimization with limited feedback, and it attains tight regret bounds in the
intermittent communication setting for both first and zeroth-order feedback.
Our results thus bridge the gap between stochastic and adaptive settings in
federated online optimization
Energy-Efficient Power Control: A Look at 5G Wireless Technologies
This work develops power control algorithms for energy efficiency (EE)
maximization (measured in bit/Joule) in wireless networks. Unlike previous
related works, minimum-rate constraints are imposed and the
signal-to-interference-plus-noise ratio takes a more general expression, which
allows one to encompass some of the most promising 5G candidate technologies.
Both network-centric and user-centric EE maximizations are considered. In the
network-centric scenario, the maximization of the global EE and the minimum EE
of the network are performed. Unlike previous contributions, we develop
centralized algorithms that are guaranteed to converge, with affordable
computational complexity, to a Karush-Kuhn-Tucker point of the considered
non-convex optimization problems. Moreover, closed-form feasibility conditions
are derived. In the user-centric scenario, game theory is used to study the
equilibria of the network and to derive convergent power control algorithms,
which can be implemented in a fully decentralized fashion. Both scenarios above
are studied under the assumption that single or multiple resource blocks are
employed for data transmission. Numerical results assess the performance of the
proposed solutions, analyzing the impact of minimum-rate constraints, and
comparing the network-centric and user-centric approaches.Comment: Accepted for Publication in the IEEE Transactions on Signal
Processin
Cross-layer Congestion Control, Routing and Scheduling Design in Ad Hoc Wireless Networks
This paper considers jointly optimal design of crosslayer congestion control, routing and scheduling for ad hoc
wireless networks. We first formulate the rate constraint and scheduling constraint using multicommodity flow variables, and formulate resource allocation in networks with fixed wireless channels (or single-rate wireless devices that can mask channel variations) as a utility maximization problem with these constraints.
By dual decomposition, the resource allocation problem
naturally decomposes into three subproblems: congestion control,
routing and scheduling that interact through congestion price.
The global convergence property of this algorithm is proved. We
next extend the dual algorithm to handle networks with timevarying
channels and adaptive multi-rate devices. The stability
of the resulting system is established, and its performance is
characterized with respect to an ideal reference system which
has the best feasible rate region at link layer.
We then generalize the aforementioned results to a general
model of queueing network served by a set of interdependent
parallel servers with time-varying service capabilities, which
models many design problems in communication networks. We
show that for a general convex optimization problem where a
subset of variables lie in a polytope and the rest in a convex set,
the dual-based algorithm remains stable and optimal when the
constraint set is modulated by an irreducible finite-state Markov
chain. This paper thus presents a step toward a systematic way
to carry out cross-layer design in the framework of ālayering as
optimization decompositionā for time-varying channel models
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