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Found 5 papers, 1 papers with code
A Communication and Computation Efficient Fully First-order Method for Decentralized Bilevel Optimization
Bilevel optimization, crucial for hyperparameter tuning, meta-learning and reinforcement learning, remains less explored in the decentralized learning paradigm, such as decentralized federated learning (DFL).
AxiomVision: Accuracy-Guaranteed Adaptive Visual Model Selection for Perspective-Aware Video Analytics
The rapid evolution of multimedia and computer vision technologies requires adaptive visual model deployment strategies to effectively handle diverse tasks and varying environments.
Decentralized Stochastic Proximal Gradient Descent with Variance Reduction over Time-varying Networks
By transforming our decentralized algorithm into a centralized inexact proximal gradient algorithm with variance reduction, and controlling the bounds of error sequences, we prove that DPSVRG converges at the rate of $O(1/T)$ for general convex objectives plus a non-smooth term with $T$ as the number of iterations, while DSPG converges at the rate $O(\frac{1}{\sqrt{T}})$.
Toward Packet Routing with Fully-distributed Multi-agent Deep Reinforcement Learning
Packet routing is one of the fundamental problems in computer networks in which a router determines the next-hop of each packet in the queue to get it as quickly as possible to its destination.
Deep Reinforcement Learning for Multi-Resource Multi-Machine Job Scheduling
Minimizing job scheduling time is a fundamental issue in data center networks that has been extensively studied in recent years.
