mlsanityData to 7 Oct 2026

Communication-Efficient and Personalized Federated Lottery Ticket Learning

arXiv 2104.12501 ↗First posted 26 Apr 2021cs.LG cs.IT cs.NIcited by 10

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Abstract (arXiv)

The lottery ticket hypothesis (LTH) claims that a deep neural network (i.e., ground network) contains a number of subnetworks (i.e., winning tickets), each of which exhibiting identically accurate inference capability as that of the ground network. Federated learning (FL) has recently been applied in LotteryFL to discover such winning tickets in a distributed way, showing higher accuracy multi-task learning than Vanilla FL. Nonetheless, LotteryFL relies on unicast transmission on the downlink, and ignores mitigating stragglers, questioning scalability. Motivated by this, in this article we propose a personalized and communication-efficient federated lottery ticket learning algorithm, coined CELL, which exploits downlink broadcast for communication efficiency. Furthermore, it utilizes a novel user grouping method, thereby alternating between FL and lottery learning to mitigate stragglers. Numerical simulations validate that CELL achieves up to 3.6% higher personalized task classification accuracy with 4.3x smaller total communication cost until convergence under the CIFAR-10 dataset.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 12 of its 17 references that are in the map. First posted on arXiv 26 Apr 2021.

Builds on

12 of 17 references.

References · 12

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networkscited by 2,250
  3. 2017Federated Multi-Task Learningcited by 669
  4. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  5. 2019Rigging the Lottery: Making All Tickets Winnerscited by 491
  6. 2020Supermasks in Superpositioncited by 223
  7. 2019Deconstructing Lottery Tickets: Zeros, Signs, and the Supermaskcited by 206
  8. 2018Wireless Network Intelligence at the Edgecited by 90
  9. 2019Robust Federated Learning in a Heterogeneous Environmentcited by 88
  10. 2020Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applicationscited by 62
  11. 2020LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasetscited by 60
  12. 2020A Public Safety Framework for Immersive Aerial Monitoring through 5G Commercial Networkcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 4; 2025: 2; 2026: 1.

10 papers.

  1. 2022Sparse Random Networks for Communication-Efficient Federated Learningcited by 32
  2. 2024A Survey of Lottery Ticket Hypothesiscited by 23
  3. 2024FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extractioncited by 21
  4. 2025A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learningcited by 15
  5. 2024Synergizing Foundation Models and Federated Learning: A Surveycited by 14
  6. 2023Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computingcited by 5
  7. 2024SSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated Learningcited by 1
  8. 2025Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneitycited by 1
  9. 2021Communication and Energy Efficient Slimmable Federated Learning via Superposition Coding and Successive Decodingcited by 0
  10. 2026Representation-Aligned Multi-Scale Personalization for Federated Learningcited by 0