More Like this

1. One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning

   Blum, A.; Haghtalab, N.; Phillips, R.L.; Shao, H. (January 2021, International Conference on Machine Learning)

   null (Ed.)

   In recent years, federated learning has been embraced as an approach for bringing about collaboration across large populations of learning agents. However, little is known about how collaboration protocols should take agents’ incentives into account when allocating individual resources for communal learning in order to maintain such collaborations. Inspired by game theoretic notions, this paper introduces a framework for incentive-aware learning and data sharing in federated learning. Our stable and envy-free equilibria capture notions of collaboration in the presence of agents interested in meeting their learning objectives while keeping their own sample collection burden low. For example, in an envy-free equilibrium, no agent would wish to swap their sampling burden with any other agent and in a stable equilibrium, no agent would wish to unilaterally reduce their sampling burden. In addition to formalizing this framework, our contributions include characterizing the structural properties of such equilibria, proving when they exist, and showing how they can be computed. Furthermore, we compare the sample complexity of incentive-aware collaboration with that of optimal collaboration when one ignores agents’ incentives. 

   more » « less
2. One Diffusion to Generate Them All

   Le, D H; Pham, T; Lee, S; Clark, C; Kembhavi, A; Mandt, S; Krishna, R; Lu, J (July 2025, International Conference on Computer Vision and Pattern Recognition (CVPR))
3. One Diffusion to Generate Them All

   Le, D H; Pham, T; Lee, S; Clark, C; Kembhavi, A; Mandt, S; Krishna, R; Lu, J (July 2025, International Conference on Computer Vision and Pattern Recognition (CVPR))
4. An All-in-One Bioinspired Neural Network

   https://doi.org/10.1021/acsnano.2c02172  

   Subbulakshmi Radhakrishnan, Shiva; Dodda, Akhil; Das, Saptarshi (December 2022, ACS Nano)
5. OPA: One-Predict-All For Efficient Deployment

   Junpeng Guo; Shengqing Xia; Chunyi Peng (January 2023, INFOCOM)

   Deep neural network (DNN) is the de facto standard for running a variety of computer vision applications over mobile and embedded systems. Prior to deployment, a DNN is specialized by training to fit the target use scenario (depending on computing power and visual data input). To handle its costly training and meet diverse deployment needs, a “Train Once, Deploy Everywhere” paradigm has been recently proposed by training one super-network and selecting one out of many sub-networks (part of the super-network) for the target scenario; This empowers efficient DNN deployment at low training cost (training once). However, the existing studies tackle some deployment factors like computing power and source data but largely overlook the impact of their runtime dynamics (say, time-varying visual contents and GPU/CPU workloads). In this work, we propose OPA to cover all these deployment factors, particularly those along with runtime dynamics in visual data contents and computing resources. To quickly and accurately learn which sub-network runs “best” in the dynamic deployment scenario, we devise a “One-Predict-All” approach with no need to run all the candidate sub-networks. Instead, we first develop a shallow sub-network to test the water and then use its test results to predict the performance of all other deeper sub-networks. We have implemented and evaluated OPA. Compared to the state-of-the-art, OPA has achieved up to 26% higher Top-1 accuracy for a given latency requirement. 

   more » « less