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Found 7 papers, 2 papers with code
Order Optimal Bounds for One-Shot Federated Learning over non-Convex Loss Functions
We then prove that this lower bound is order optimal in $m$ and $n$ by presenting a distributed learning algorithm, called Multi-Resolution Estimator for Non-Convex loss function (MRE-NC), whose expected loss matches the lower bound for large $mn$ up to polylogarithmic factors.
Distributed Fair Scheduling for Information Exchange in Multi-Agent Systems
Moreover, our scheduling algorithm adjusts itself dynamically to achieve a high throughput at the same time.
Fairness
Multiagent Systems
Networking and Internet Architecture
Performance
Bounds on Over-Parameterization for Guaranteed Existence of Descent Paths in Shallow ReLU Networks
We show that there exist poor local minima with positive curvature for some training sets of size $n\geq m+2d-2$.
Order Optimal One-Shot Distributed Learning
We propose an algorithm called Multi-Resolution Estimator (MRE) whose expected error is no larger than $\tilde{O}\big(m^{-{1}/{\max(d, 2)}} n^{-1/2}\big)$, where $d$ is the dimension of the parameter space.
One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them
We investigate the impact of communication constraint, $B$, on the expected error and derive a tight lower bound on the error achievable by any algorithm.
Distributed Voting/Ranking with Optimal Number of States per Node
Considering a network with $n$ nodes, where each node initially votes for one (or more) choices out of $K$ possible choices, we present a Distributed Multi-choice Voting/Ranking (DMVR) algorithm to determine either the choice with maximum vote (the voting problem) or to rank all the choices in terms of their acquired votes (the ranking problem).
Token-based Function Computation with Memory
In this paper, we propose a novel token-based approach to compute a wide class of target functions to which we refer as "Token-based function Computation with Memory" (TCM) algorithm.
