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RelaySum for Decentralized Deep Learning on Heterogeneous Data
Thijs Vogels · Lie He · Anastasiia Koloskova · Sai Praneeth Karimireddy · Tao Lin · Sebastian Stich · Martin Jaggi

Wed Dec 08 12:30 AM -- 02:00 AM (PST) @ None #None

In decentralized machine learning, workers compute model updates on their local data.Because the workers only communicate with few neighbors without central coordination, these updates propagate progressively over the network.This paradigm enables distributed training on networks without all-to-all connectivity, helping to protect data privacy as well as to reduce the communication cost of distributed training in data centers.A key challenge, primarily in decentralized deep learning, remains the handling of differences between the workers' local data distributions.To tackle this challenge, we introduce the RelaySum mechanism for information propagation in decentralized learning.RelaySum uses spanning trees to distribute information exactly uniformly across all workers with finite delays depending on the distance between nodes.In contrast, the typical gossip averaging mechanism only distributes data uniformly asymptotically while using the same communication volume per step as RelaySum.We prove that RelaySGD, based on this mechanism, is independent of data heterogeneity and scales to many workers, enabling highly accurate decentralized deep learning on heterogeneous data.

Author Information

Thijs Vogels (EPFL)
Lie He (EPFL)
Anastasiia Koloskova (EPFL)
Sai Praneeth Karimireddy (EPFL)

I am a second year PhD student working in convex and non-convex optimization with Prof. Martin Jaggi. My focus is on designing faster and more scalable optimization algorithms for machine learning. Some of my preliminary results and problems I am currently working on- 1. Robust accelerated algorithms - Nesterov acceleration modified to be robust to noise. 2. Faster algorithms which take second order information about the function into account. 3. A $O(1/t^2)$ rate *affine invariant* algorithm for constrained optimization. 4. Frank-Wolfe algorithm for non-smooth functions using 'noisy-smoothing'

Tao Lin (EPFL)
Sebastian Stich (CISPA)

Dr. [Sebastian U. Stich](https://sstich.ch/) is a faculty at the CISPA Helmholtz Center for Information Security. Research interests: - *methods for machine learning and statistics*—at the interface of theory and practice - *collaborative learning* (distributed, federated and decentralized methods) - *optimization for machine learning* (adaptive stochastic methods and generalization performance)

Martin Jaggi (EPFL)

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