FedFIbOS: Fisher Importance based Optimal Submodelling for Heterogeneous Federated Learning

arXiv:2609.19559 · cs.LG · Submitted 2026-09-17 · Read on arXiv

cs.LG

Submitted: 2026-09-17

Updated: 2026-09-17

Comments: 9 pages, 4 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Federated Learning — 32× in this paper · explained in 14 episodes
• Fisher Information — 20× in this paper · explained in 3 episodes
• Accuracy — 15× in this paper · explained in 1 episode
• Non-IID Data — 6× in this paper · explained in 2 episodes
• Pruning — 6× in this paper · explained in 4 episodes
• Data Heterogeneity — 3× in this paper · explained in 3 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Submodel Selection Problem — 2× in this paper
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Convergence Guarantees — 1× in this paper · explained in 3 episodes
• Differential Privacy — 1× in this paper · explained in 4 episodes
• Federated Learning (FL) — 1× in this paper · explained in 7 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Local Optimization — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes

Sources

• FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
• LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets

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