Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T15:46:47.779439Z
Paper Citation Record · LEDGER
As of 11 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2501.13790.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T15:46:47.779439Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
47 of 47 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation a37cd258-0359-4903-94ac-22a3b6a7f09f · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Communication complexity of distributed convex learning and optimization
Reference 1
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Observation 14e2327a-b59b-46a0-a82d-b70593fb9fc1 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression
Reference 2
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Observation 7995ca07-4999-47ab-9ebe-20f901db577e · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Distributed learning, communication complexity and privacy
Reference 3
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Observation c6b5e213-be08-4ffe-ace2-cf483f29e582 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Gradient descent on neural networks typically occurs at the edge of stability
Reference 4
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Observation 5f460f20-dc5c-4b94-8209-12d2be8c0ff0 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Optimal distributed online prediction using mini-batches
Reference 5
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Observation 1464e2d9-3b0d-4262-9578-506d6912199c · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization i: A generic algorithmic framework
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Observation 72110b65-9ec8-4a0b-8045-b9ec7801e4ee · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Sharp bounds for federated averaging (local sgd) and continuous perspective
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Observation 9775e904-e1c6-49cf-933b-fa760af9f3db · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Characterizing implicit bias in terms of optimization geometry
Reference 8
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Observation 5735e215-e8a2-4bc7-b6c9-ab02ee3637a4 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression On the Convergence of Local Descent Methods in Federated Learning
Reference 9
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Observation f899152c-e320-4cce-96d6-28bf9fec06b1 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Deep residual learning for image recognition
Reference 10
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Observation b2c9ffab-6320-4584-a086-e916b71a501e · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Risk and parameter convergence of logistic regression
Reference 11
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Observation 242c0e72-40cd-49f0-a785-47ef71b66b3e · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Fast margin maximization via dual acceleration
Reference 12
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Observation 3a99c2c4-1754-4a19-adb6-94d178ce570e · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Advances and Open Problems in Federated Learning
Reference 13
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Observation 6b6cf274-2572-496d-bf60-7e666961a3d0 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Advances and open problems in federated learning
Reference 14
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Observation 69e8a5cd-4fa6-47a3-a9fc-f8fb23fe347d · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Scaffold: Stochastic controlled averaging for federated learning
Reference 15
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Observation 8ac92962-002d-49d2-8b24-a9656cc158c8 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Tighter theory for local sgd on identical and heterogeneous data
Reference 16
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Observation cfcc2ec2-8ecb-48bb-9d7d-a24906acdd30 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression A unified theory of decentralized sgd with changing topology and local updates
Reference 17
Source-reported events for the cited work
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Observation dc729fa8-7f92-4226-a8fc-6e9231dcd9d0 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression SLowcal-SGD: Slow Query Points Improve Local-SGD for Stochastic Convex Optimization
Reference 18
Source-reported events for the cited work
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Observation 3f0cec6c-dcf2-4ebd-85d7-d1072a72e2b6 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Don't use large mini-batches, use local sgd
Reference 19
Source-reported events for the cited work
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Observation aa8eaeea-d511-4d82-bf74-fb571c48af1b · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Efficient large-scale distributed training of conditional maximum entropy models
Reference 20
Source-reported events for the cited work
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Observation 96cd270d-3102-41be-a050-7891f00f85a0 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Distributed training strategies for the structured perceptron
Reference 21
Source-reported events for the cited work
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Observation 20ae09e3-7989-40c6-a7e7-f26d405b2170 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Communication-Efficient Learning of Deep Networks from Decentralized Data
Reference 22
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Observation 64a299ee-01aa-440a-8d6b-69a7f7df0c93 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Proximal and federated random reshuffling
Reference 23
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Observation 4ee241fb-8002-4e79-9b36-030cd3ccccd8 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Stochastic gradient descent on separable data: Exact convergence with a fixed learning rate
Reference 24
Source-reported events for the cited work
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Observation 974679a0-77bd-401d-8225-5c21af772912 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Introductory lectures on convex optimization: A basic course, volume 87
Reference 25
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Observation bfe2ed56-1f9b-4ae9-9c98-8d512f74c33e · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression A minimizer far, far away, 2024
Reference 26
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Observation 25efdabe-0ee7-4f86-9977-fd8b358108a6 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression On the still unreasonable effectiveness of federated averaging for heterogeneous distributed learning
Reference 27
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 10bbef97-0329-4b30-b7dc-b2e5323eb478 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression The limits and potentials of local sgd for distributed heterogeneous learning with intermittent communication
Reference 28
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation f9867f78-3f7c-4625-b054-5fa0441dbce3 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Distributed stochastic optimization and learning
Reference 29
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 564d3391-36df-4ad6-89ea-b3bdd871161c · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression The implicit bias of gradient descent on separable data
Reference 30
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation f323f905-a048-488b-bfd0-aa2ff7383e44 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Local SGD Converges Fast and Communicates Little
Reference 31
Source-reported events for the cited work
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Observation 464b7f8b-05a1-4998-8e1a-153c4fc2915b · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Local sgd converges fast and communicates little
Reference 32
Source-reported events for the cited work
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Observation 7a377e9c-0abb-4518-8895-df6bdfd23892 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression A Field Guide to Federated Optimization
Reference 33
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Observation 17fcfc90-36da-4d52-a850-ee1730236330 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data
Reference 34
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Observation 13d71d0d-e820-4a32-9a49-338c6da5da02 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Is local sgd better than minibatch sgd? In International Conference on Machine Learning, pp.\ 10334--10343
Reference 35
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Observation b1f52b1e-22fb-49fd-973e-1aae0fdfd9cb · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Graph oracle models, lower bounds, and gaps for parallel stochastic optimization
Reference 36
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Observation cf8eb43e-7e2d-4ed7-86dc-ce8eec676e01 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Minibatch vs local sgd for heterogeneous distributed learning
Reference 37
Source-reported events for the cited work
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Observation 21cd2931-a870-434a-9d94-9675a0976255 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression The min-max complexity of distributed stochastic convex optimization with intermittent communication
Reference 38
Source-reported events for the cited work
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Observation 5f3e58ba-060e-4578-9d32-bba7b4217b6c · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency
Reference 39
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Observation 77fe3839-79f6-4fc3-afc3-78cd4a5a9f29 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Implicit bias of gradient descent for logistic regression at the edge of stability
Reference 40
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Observation 52e2efb2-1d9f-4873-bd65-a3dec788e6e9 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Federated accelerated stochastic gradient descent
Reference 41
Source-reported events for the cited work
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Observation 4b77c0b8-8d0c-4336-8b34-b57cdf1cc861 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Information-theoretic lower bounds for distributed statistical estimation with communication constraints
Reference 42
Source-reported events for the cited work
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Observation ab1d4959-65a6-4dc9-a913-ee131a5b127e · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Parallelized stochastic gradient descent
Reference 43
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Observation 2850221d-1a0f-49c3-b126-61a33c5ac142 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression write newline
Reference 44
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Observation ab9a25ce-ec6d-44ad-8bbf-1353bd0d166e · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression @esa (Ref
Reference 45
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Observation 9ba25018-7fca-4301-b6f8-80f5a964ad88 · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Unresolved cited work
Reference 46
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Observation c3b30e2b-313e-4239-83d7-e625c6d3e92a · outbound
Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Unresolved cited work
Reference 47
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Unavailable: canonical work link unavailable.
No inbound Pith citation observations are available.