Timezone: »
In the traditional federated learning setting, a central server coordinates a network of clients to train one global model. However, the global model may serve many clients poorly due to data heterogeneity. Moreover, there may not exist a trusted central party that can coordinate the clients to ensure that each of them can benefit from others. To address these concerns, we present a novel decentralized framework, FedeRiCo, where each client can learn as much or as little from other clients as is optimal for its local data distribution. Based on expectation-maximization, FedeRiCo estimates the utilities of other participants’ models on each client’s data so that everyone can select the right collaborators for learning. As a result, our algorithm outperforms other federated, personalized, and/or decentralized approaches on several benchmark datasets, being the only approach that consistently performs better than training with local data only.
Author Information
Yi Sui (Layer 6 AI)
Junfeng Wen (University of Alberta)
Yenson Lau (Columbia University)
Brendan Ross (Layer 6 AI)
Jesse Cresswell (Layer 6 AI)
More from the Same Authors
-
2022 : CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds »
Jesse Cresswell · Brendan Ross · Gabriel Loaiza-Ganem · Humberto Reyes-Gonzalez · Marco Letizia · Anthony Caterini -
2022 : The Union of Manifolds Hypothesis »
Bradley Brown · Anthony Caterini · Brendan Ross · Jesse Cresswell · Gabriel Loaiza-Ganem -
2022 : Denoising Deep Generative Models »
Gabriel Loaiza-Ganem · Brendan Ross · Luhuan Wu · John Cunningham · Jesse Cresswell · Anthony Caterini -
2023 Poster: Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models »
George Stein · Jesse Cresswell · Rasa Hosseinzadeh · Yi Sui · Brendan Ross · Valentin Villecroze · Zhaoyan Liu · Anthony Caterini · Eric Taylor · Gabriel Loaiza-Ganem -
2022 : Disparate Impact in Differential Privacy from Gradient Misalignment »
Maria Esipova · Atiyeh Ashari · Yaqiao Luo · Jesse Cresswell -
2021 Poster: Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows »
Brendan Ross · Jesse Cresswell -
2021 Poster: Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models »
Yi Sui · Ga Wu · Scott Sanner