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Deviation of local-SGD workers from their virtual mean
Statement
Let be integers and let local SGD use integer synchronization times , with . Let . For each , define the previous synchronization time . Suppose the step sizes are positive and nonincreasing, and for each . Suppose also where is the local batch gradient. Then, for every , At , the average deviation is zero because is a synchronization time.
Statement references
Facts & Assumptions
Given: The local-SGD trajectories, synchronization schedule and local gradient second-moment bound in the Statement.
Local SGD with periodic synchronization: all workers have the same iterate at each synchronization time, including the initial time .
Relaxed inequalities for squared Euclidean norms: squared norms of finite vector sums obey the stated relaxed inequality.
Expectation of a nonnegative or integrable random variable: expectation is taken over the finite product law of the sampled indices.
Proof
Fix and let be the latest synchronization time not exceeding [given] . Then and [F1] gives for all . For any vectors and any , expansion around their mean gives Apply this identity with and the common value to bound the worker deviation by the mean squared displacement from time .
For each worker, [given] . By [F2], nonincreasing step sizes, and the second-moment hypothesis, Average over and use from the Statement. The resulting bound is , proving the claim.
Depends on
- Local SGD with periodic synchronization
- Relaxed inequalities for squared Euclidean norms
- Expectation of a nonnegative or integrable random variable
- Linearity, monotonicity, and the modulus bound for expectation
- Expectation agrees with the published finite weighted sum
- The Euclidean inner product $\langle x,y\rangle = \sum_{k<n} x_k y_k$ on $\mathbb{R}^n$
Used by
Dependency tree · two levels
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Sources
- Sebastian U. Stich, Local SGD Converges Fast and Communicates Little, Lemma 3.3 and Appendix A (standard reference, not scraped)