Uploaded July 2026 | Updated September 2026, 2 weeks ago
FLASC is a composite method combining LoRA with sparse Top-K communication for finetuning models in communication-constrained federated learning, avoiding the accuracy and cost problems of prior sparse-LoRA approaches. Across four FL datasets it outperforms existing sparse-LoRA methods with up to 20% higher accuracy or 10× less communication, plus an efficient rank-and-sparsity search.
Speakers & affiliations: Kevin Kuo (Carnegie Mellon University), Arian Raje (Carnegie Mellon University), Kousik Rajesh (Carnegie Mellon University), Virginia Smith (Carnegie Mellon University)
Session: Paper Session 6 — Learning & Control · Thursday, May 28
Key terms: federated learning, LoRA, sparse communication, parameter-efficient finetuning, compression, communication cost
ACM Digital Library: doi.org/10.1145/3786335.3813151
FLASC is a composite method combining LoRA with sparse Top-K communication for finetuning models in communication-constrained federated learning, avoiding the accuracy and cost problems of prior sparse-LoRA approaches. Across four FL datasets it outperforms existing sparse-LoRA methods with up to 20% higher accuracy or 10× less communication, plus an efficient rank-and-sparsity search.
Speakers & affiliations: Kevin Kuo (Carnegie Mellon University), Arian Raje (Carnegie Mellon University), Kousik Rajesh (Carnegie Mellon University), Virginia Smith (Carnegie Mellon University)
Session: Paper Session 6 — Learning & Control · Thursday, May 28
Key terms: federated learning, LoRA, sparse communication, parameter-efficient finetuning, compression, communication cost
ACM Digital Library: doi.org/10.1145/3786335.3813151
