Research map / Federated learning and privacy
Communication-efficient FL: research map
382 accepted papers on Communication-efficient FL in Federated learning and privacy, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 2 clusters and 6 approaches. The busiest year so far is 2024.
Within Federated learning and privacy, its share shrank from 19.9% in 2023–24 to 12.7% in 2025–26 (148 → 116 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).
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Approaches and key papers
optimization · convex · fedavg · 199 papers
Approaches in this cluster:
- Client participation analysis (68 papers)
Prove convergence of federated averaging under client heterogeneity and arbitrary participation. - Federated optimization algorithms (70 papers)
Develop faster local-update and composite or minimax optimizers for federated learning. - Local drift correction (61 papers)
Regularize or reaggregate local training to counter heterogeneity and forgetting in federated learning.
Most cited and most cited since 2024:
- SCAFFOLD: Stochastic Controlled Averaging for Federated Learning (ICML 2020 · 709 citations)
- Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization (NeurIPS 2020 · 565 citations)
- Compressed and distributed least-squares regression: convergence rates with applications to federated learning (ICML 2025 · 104 citations)
- Communication-Efficient Federated Learning with Accelerated Client Gradient (CVPR 2024 · 38 citations)
devices · edge · dynamic · 183 papers
Approaches in this cluster:
- Efficient local training in FL (77 papers)
Cut federated training cost with low precision, few rounds and decoupled local learning. - Communication-efficient heterogeneous FL (65 papers)
Reduce communication via dynamic regularization, sparse training and heterogeneity-aware client sampling. - Decentralized peer-to-peer training (41 papers)
Train over decentralized data using asynchronous updates and compressed communication.
Most cited and most cited since 2024:
- SplitFed: When Federated Learning Meets Split Learning (AAAI 2022 · 696 citations)
- HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients (ICLR 2021 · 192 citations)
- FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-Aware Model Update (AAAI 2024 · 73 citations)
- Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices (CVPR 2024 · 23 citations)