mlsanityData to 7 Oct 2026

Stochastic Gradient-Push for Strongly Convex Functions on Time-Varying Directed Graphs

arXiv 1406.2075 ↗First posted 9 Jun 2014math.OC cs.SY eess.SYcited by 31

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Abstract (arXiv)

We investigate the convergence rate of the recently proposed subgradient-push method for distributed optimization over time-varying directed graphs. The subgradient-push method can be implemented in a distributed way without requiring knowledge of either the number of agents or the graph sequence; each node is only required to know its out-degree at each time. Our main result is a convergence rate of O left((ln t)/t right) for strongly convex functions with Lipschitz gradients even if only stochastic gradient samples are available; this is asymptotically faster than the O left((ln t)/√t right) rate previously known for (general) convex functions.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jun 2014.

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Citing papers per year: 2021: 5; 2022: 8; 2023: 4; 2024: 9; 2025: 2; 2026: 3.

31 papers.

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  1. 2021Decentralized Federated Learning through Proxy Model Sharingcited by 56
  2. 2021RelaySum for Decentralized Deep Learning on Heterogeneous Datacited by 39
  3. 2021Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devicescited by 36
  4. 2023Epidemic Learning: Boosting Decentralized Learning with Randomized Communicationcited by 21
  5. 2021Improving the Transient Times for Distributed Stochastic Gradient Methodscited by 13
  6. 2021Accelerated Gradient Tracking over Time-varying Graphs for Decentralized Optimizationcited by 11
  7. 2022Decentralized Stochastic Optimization with Inherent Privacy Protectioncited by 11
  8. 2024Noiseless Privacy-Preserving Decentralized Learningcited by 9
  9. 2024Decentralized Directed Collaboration for Personalized Federated Learningcited by 9
  10. 2022On Generalization of Decentralized Learning with Separable Datacited by 8
  11. 2023On the Tradeoff between Privacy Preservation and Byzantine-Robustness in Decentralized Learningcited by 8
  12. 2022Quantization enabled Privacy Protection in Decentralized Stochastic Optimizationcited by 7
19 more
  1. 2024Boosting Asynchronous Decentralized Learning with Model Fragmentationcited by 6
  2. 2024DRACO: Decentralized Asynchronous Federated Learning over Row-Stochastic Wireless Networkscited by 5
  3. 2023Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the Presence of Noisecited by 4
  4. 2024FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learningcited by 4
  5. 2023Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updatescited by 3
  6. 2024Decentralized Federated Learning with Gradient Tracking over Time-Varying Directed Networkscited by 3
  7. 2022Variance reduced stochastic optimization over directed graphs with row and column stochastic weightscited by 2
  8. 2022Locally Asynchronous Stochastic Gradient Descent for Decentralised Deep Learningcited by 1
  9. 2022On Arbitrary Compression for Decentralized Consensus and Stochastic Optimization over Directed Networkscited by 1
  10. 2022Push--Pull with Device Samplingcited by 1
  11. 2022Decentralized Hyper-Gradient Computation over Time-Varying Directed Networkscited by 1
  12. 2024Fair Decentralized Learningcited by 1
  13. 2024Fast Butterfly-Core Community Search For Large Labeled Graphscited by 0
  14. 2024MUSIC: Accelerated Convergence for Distributed Optimization With Inexact and Exact Methodscited by 0
  15. 2025DFCA: Decentralized Federated Clustering Algorithmcited by 0
  16. 2025A Unified Convergence Analysis for Semi-Decentralized Learning: Sampled-to-Sampled vs. Sampled-to-All Communicationcited by 0
  17. 2026Stability and Generalization of Push-Sum Based Decentralized Optimization over Directed Graphscited by 0
  18. 2026Improved Convergence Analysis of Topology Dependence in Decentralized SGDcited by 0
  19. 2026Decentralized Gradient Descent: Bottleneck Regimes and Budget Complexitycited by 0