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<i>Distributed learning</i> protocols are designed to train on distributed data without gathering it all on a single centralized machine, thus contributing to the efficiency of the system and enhancing its privacy. We study a central problem in distributed learning, called {\it distributed learning of halfspaces}: let $U \subseteq \mathbb{R}^d$ be a known domain of size $n$ and let $h:\mathbb{R}^d\to \mathbb{R}$ be an unknown target affine function.\footnote{In practice, the domain $U$ is defined implicitly by the representation of $d$-dimensional vectors which is used in the protocol.} A set of <i>examples</i> $\{(u,b)\}$ is distributed between several parties, where~$u \in U$ is a point and $b = \mathsf{sign}(h(u)) \in \{\pm 1\}$ is its label. The parties goal is to agree on a classifier~$f: U\to\{\pm 1\}$ such that~$f(u)=b$ for every input example~$(u,b)$. We design a protocol for the distributed halfspace learning problem in the two-party setting, communicating only $\tilde O(d\log n)$ bits. To this end, we introduce a new tool called <i>halfspace containers</i>, that is closely related to <i>bracketing numbers</i> in statistics and to <i>hyperplane cuttings</i> in discrete geometry, and allows for a compressed approximate representation of every halfspace. We complement our upper bound result by an almost matching $\tilde \Omega(d\log n)$ lower bound on the communication complexity of any such protocol Since the distributed halfspace learning problem is closely related to the <i>convex set disjointness</i> problem in communication complexity and the problem of <i>distributed linear programming</i> in distributed optimization, we also derive upper and lower bounds of $\tilde O(d^2\log n)$ and~$\tilde{\Omega}(d\log n)$ on the communication complexity of both of these basic problems.