20 citations · 49 across the 19 of their papers we have counts for
19 papers
Decoupled Split Learning via Auxiliary Loss
Anower Zihad, Felix Owino, Ming Tang +1
Split learning is a distributed training paradigm where a neural network is partitioned between clients and a server, which allows data to remain at the client while only intermedi…
Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach
Fnu Hairi, Jiao Yang, Tianchen Zhou +6
In many multi-objective reinforcement learning (MORL) applications, being able to systematically explore the Pareto-stationary solutions under multiple non-convex reward objectives…
STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning
Zhuqing Liu, Chaosheng Dong, Michinari Momma +5
Recently, multi-objective optimization (MOO) has gained attention for its broad applications in ML, operations research, and engineering. However, MOO algorithm design remains in i…
Do We Really Need to Design New Byzantine-robust Aggregation Rules?
Minghong Fang, Seyedsina Nabavirazavi, Zhuqing Liu +3
Federated learning (FL) allows multiple clients to collaboratively train a global machine learning model through a server, without exchanging their private training data. However,…
PSMGD: Periodic Stochastic Multi-Gradient Descent for Fast Multi-Objective Optimization
Mingjing Xu, Peizhong Ju, Jia Liu +1
Multi-objective optimization (MOO) lies at the core of many machine learning (ML) applications that involve multiple, potentially conflicting objectives (e.g., multi-task learning,…
Adversarial Attacks to Multi-Modal Models
Zhihao Dou, Xin Hu, Haibo Yang +2
Multi-modal models have gained significant attention due to their powerful capabilities. These models effectively align embeddings across diverse data modalities, showcasing superi…
Can We Theoretically Quantify the Impacts of Local Updates on the Generalization Performance of Federated Learning?
Peizhong Ju, Haibo Yang, Jia Liu +2
Federated Learning (FL) has gained significant popularity due to its effectiveness in training machine learning models across diverse sites without requiring direct data sharing. W…
Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation
Haibo Yang, Peiwen Qiu, Prashant Khanduri +2
Existing works in federated learning (FL) often assume an ideal system with either full client or uniformly distributed client participation. However, in practice, it has been obse…
Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learning
Tianchen Zhou, FNU Hairi, Haibo Yang +5
Reinforcement learning with multiple, potentially conflicting objectives is pervasive in real-world applications, while this problem remains theoretically under-explored. This pape…
Federated Multi-Objective Learning
Haibo Yang, Zhuqing Liu, Jia Liu +2
In recent years, multi-objective optimization (MOO) emerges as a foundational problem underpinning many multi-agent multi-task learning applications. However, existing algorithms i…
Taming Fat-Tailed ("Heavier-Tailed'' with Potentially Infinite Variance) Noise in Federated Learning
Haibo Yang, Peiwen Qiu, Jia Liu
A key assumption in most existing works on FL algorithms' convergence analysis is that the noise in stochastic first-order information has a finite variance. Although this assumpti…
SAGDA: Achieving Communication Complexity in Federated Min-Max Learning
Haibo Yang, Zhuqing Liu, Xin Zhang +1
To lower the communication complexity of federated min-max learning, a natural approach is to utilize the idea of infrequent communications (through multiple local updates) same as…
NET-FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data
Xin Zhang, Minghong Fang, Zhuqing Liu +3
Federated learning (FL) has received a surge of interest in recent years thanks to its benefits in data privacy protection, efficient communication, and parallel data processing. A…
CHARLES: Channel-Quality-Adaptive Over-the-Air Federated Learning over Wireless Networks
Jiayu Mao, Haibo Yang, Peiwen Qiu +2
Over-the-air federated learning (OTA-FL) has emerged as an efficient mechanism that exploits the superposition property of the wireless medium and performs model aggregation for fe…
Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control
Haibo Yang, Peiwen Qiu, Jia Liu +1
This paper considers over-the-air federated learning (OTA-FL). OTA-FL exploits the superposition property of the wireless medium, and performs model aggregation over the air for fr…
Anarchic Federated Learning
Haibo Yang, Xin Zhang, Prashant Khanduri +1
Present-day federated learning (FL) systems deployed over edge networks consists of a large number of workers with high degrees of heterogeneity in data and/or computing capabiliti…
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning
Prashant Khanduri, Pranay Sharma, Haibo Yang +4
Federated Learning (FL) refers to the paradigm where multiple worker nodes (WNs) build a joint model by using local data. Despite extensive research, for a generic non-convex FL pr…
CFedAvg: Achieving Efficient Communication and Fast Convergence in Non-IID Federated Learning
Haibo Yang, Jia Liu, Elizabeth S. Bentley
Federated learning (FL) is a prevailing distributed learning paradigm, where a large number of workers jointly learn a model without sharing their training data. However, high comm…
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning
Haibo Yang, Minghong Fang, Jia Liu
Federated learning (FL) is a distributed machine learning architecture that leverages a large number of workers to jointly learn a model with decentralized data. FL has received in…