6 papers · 1 filter
PubSwap: Public-Data Off-Policy Coordination for Federated RLVR
Anupam Nayak, Baris Askin, Muhammed Ustaomeroglu +2
Reasoning post-training with reinforcement learning from verifiable rewards (RLVR) is typically studied in centralized settings, yet many realistic applications involve decentraliz…
MELINOE: Fine-Tuning Enables Memory-Efficient Inference for Mixture-of-Experts Models
Arian Raje, Anupam Nayak, Gauri Joshi
Mixture-of-Experts (MoE) model architectures can significantly reduce the number of activated parameters per token, enabling computationally efficient training and inference. Howev…
Ravan: Multi-Head Low-Rank Adaptation for Federated Fine-Tuning
Arian Raje, Baris Askin, Divyansh Jhunjhunwala +1
Large language models (LLMs) have not yet effectively leveraged the vast amounts of edge-device data, and federated learning (FL) offers a promising paradigm to collaboratively fin…
FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA
Divyansh Jhunjhunwala, Arian Raje, Madan Ravi Ganesh +6
LoRA has emerged as one of the most promising fine-tuning techniques, especially for federated learning (FL), since it significantly reduces communication and computation costs at…
Initialization Matters: Unraveling the Impact of Pre-Training on Federated Learning
Divyansh Jhunjhunwala, Pranay Sharma, Zheng Xu +1
Initializing with pre-trained models when learning on downstream tasks is becoming standard practice in machine learning. Several recent works explore the benefits of pre-trained i…
Erasure Coded Neural Network Inference via Fisher Averaging
Divyansh Jhunjhunwala, Neharika Jali, Gauri Joshi +1
Erasure-coded computing has been successfully used in cloud systems to reduce tail latency caused by factors such as straggling servers and heterogeneous traffic variations. A majo…