3 research outputs found
Tight Bounds for Collaborative PAC Learning via Multiplicative Weights
We study the collaborative PAC learning problem recently proposed in Blum et
al.~\cite{BHPQ17}, in which we have players and they want to learn a target
function collaboratively, such that the learned function approximates the
target function well on all players' distributions simultaneously. The quality
of the collaborative learning algorithm is measured by the ratio between the
sample complexity of the algorithm and that of the learning algorithm for a
single distribution (called the overhead). We obtain a collaborative learning
algorithm with overhead , improving the one with overhead in \cite{BHPQ17}. We also show that an overhead is
inevitable when is polynomial bounded by the VC dimension of the hypothesis
class. Finally, our experimental study has demonstrated the superiority of our
algorithm compared with the one in Blum et al. on real-world datasets.Comment: Accepted to NIPS 2018. 14 page
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning
In recent years, federated learning has been embraced as an approach for
bringing about collaboration across large populations of learning agents.
However, little is known about how collaboration protocols should take agents'
incentives into account when allocating individual resources for communal
learning in order to maintain such collaborations. Inspired by game theoretic
notions, this paper introduces a framework for incentive-aware learning and
data sharing in federated learning. Our stable and envy-free equilibria capture
notions of collaboration in the presence of agents interested in meeting their
learning objectives while keeping their own sample collection burden low. For
example, in an envy-free equilibrium, no agent would wish to swap their
sampling burden with any other agent and in a stable equilibrium, no agent
would wish to unilaterally reduce their sampling burden.
In addition to formalizing this framework, our contributions include
characterizing the structural properties of such equilibria, proving when they
exist, and showing how they can be computed. Furthermore, we compare the sample
complexity of incentive-aware collaboration with that of optimal collaboration
when one ignores agents' incentives
Collaborative Top Distribution Identifications with Limited Interaction
We consider the following problem in this paper: given a set of
distributions, find the top- ones with the largest means. This problem is
also called {\em top- arm identifications} in the literature of
reinforcement learning, and has numerous applications. We study the problem in
the collaborative learning model where we have multiple agents who can draw
samples from the distributions in parallel. Our goal is to characterize the
tradeoffs between the running time of learning process and the number of rounds
of interaction between agents, which is very expensive in various scenarios. We
give optimal time-round tradeoffs, as well as demonstrate complexity
separations between top- arm identification and top- arm identifications
for general and between fixed-time and fixed-confidence variants. As a
byproduct, we also give an algorithm for selecting the distribution with the
-th largest mean in the collaborative learning model.Comment: Accepted for presentation at FOCS 202