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Clustered federated learning (FL) has been shown to produce promising results by grouping clients into clusters.
This is especially effective in scenarios where separate groups of clients have significant differences in the distributions of their local data. Existing clustered FL algorithms are essentially trying to group together clients with similar distributions so that clients in the same cluster can leverage each other&#39;s data to better perform federated learning. However, prior clustered FL algorithms attempt to learn these distribution similarities indirectly during training, which can be quite time consuming as many rounds of federated learning may be required until the formation of clusters is stabilized. In this paper, we propose a new approach to federated learning that directly aims to efficiently identify distribution similarities among clients by analyzing the principal angles between the client data subspaces. Each client applies a truncated singular value decomposition (SVD) step on its local data in a single-shot manner to derive a small set of principal vectors, which provides a signature that succinctly captures the main characteristics of the underlying distribution.
This small set of principal vectors is provided to the server so that the server can directly identify distribution similarities among the clients to form clusters.
This is achieved by comparing the similarities of the principal angles between the client data subspaces spanned by those principal vectors. The approach provides a simple, yet effective clustered FL framework that addresses a broad range of data heterogeneity issues beyond simpler forms of Non-IIDness like label skews. Our clustered FL approach also enables convergence guarantees for non-convex objectives.

AAAI 2023

Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces

non-iid

federated learning

clustering

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Clustered federated learning (FL) has been shown to produce promising results by grouping clients into clusters.
This is especially effective in scenarios where separate groups of clients have significant differences in the distributions of their local data. Existing clustered FL algorithms are essentially trying to group together clients with similar distributions so that clients in the same cluster can leverage each other's data to better perform federated learning. However, prior clustered FL algorithms attempt to learn these distribution similarities indirectly during training, which can be quite time consuming as many rounds of federated learning may be required until the formation of clusters is stabilized. In this paper, we propose a new approach to federated learning that directly aims to efficiently identify distribution similarities among clients by analyzing the principal angles between the client data subspaces. Each client applies a truncated singular value decomposition (SVD) step on its local data in a single-shot manner to derive a small set of principal vectors, which provides a signature that succinctly captures the main characteristics of the underlying distribution.
This small set of principal vectors is provided to the server so that the server can directly identify distribution similarities among the clients to form clusters.
This is achieved by comparing the similarities of the principal angles between the client data subspaces spanned by those principal vectors. The approach provides a simple, yet effective clustered FL framework that addresses a broad range of data heterogeneity issues beyond simpler forms of Non-IIDness like label skews. Our clustered FL approach also enables convergence guarantees for non-convex objectives.

poster

Stochastic gradient descent  algorithm  (SGD)  has been popular in various fields of artificial intelligence as well as a prototype of online learning algorithms. This article proposes a novel and general framework of one-sided testing for streaming data based on SGD, which  determines whether the unknown parameter is greater than a certain positive constant. We construct the online-updated test statistic sequentially by integrating the selected batch-specific estimator or its opposite, which is referred to opposite online learning. The batch-specific online estimators are chosen strategically according to the proposed sequential tactics designed by two-armed bandit process. Theoretical results prove the advantage of the strategy ensuring the distribution of test statistic to be optimal under the null hypothesis and also supply the theoretical evidence of power enhancement compared with classical test statistic. In application, the proposed method is appealing for statistical inference of one-sided testing because it is scalable for any model. Finally, the superior finite-sample performance is evaluated by simulation studies.

Opposite Online Learning via Sequentially Integrated Stochastic Gradient Descent Estimators

Tissue segmentation is a critical task in computational pathology due to its desirable ability to indicate the prognosis of cancer patients. Currently, numerous studies attempt to use image-level labels to achieve pixel-level segmentation to reduce the need for fine annotations. However, most of these methods are based on class activation map, which suffers from inaccurate segmentation boundaries. To address this problem, we propose a novel weakly-supervised tissue segmentation framework named PistoSeg, which is implemented under a fully-supervised manner by transferring tissue category labels to pixel-level masks. Firstly, a dataset synthesis method is proposed based on Mosaic transformation to generate synthesized images with pixel-level masks. Next, considering the difference between synthesized and real images, this paper devises an attention-based feature consistency, which directs the training process of a proposed pseudo-mask refining module. Finally, the refined pseudo-masks are used to train a precise segmentation model for testing. Experiments based on WSSS4LUAD and BCSS-WSSS validate that PistoSeg outperforms the state-of-the-art methods. The code is released at https://github.com/Vison307/PistoSeg. 

Weakly-Supervised Semantic Segmentation for Histopathology Images Based on Dataset Synthesis and Feature Consistency Constraint

Difference-of-Convex (DC) minimization, referring to the problem of minimizing the difference of two convex functions, has been found rich applications in statistical learning and studied extensively for decades. However, existing methods are primarily based on multi-stage convex relaxation, only leading to weak optimality of critical points. This paper proposes a coordinate descent method for minimizing a class of DC functions based on sequential nonconvex approximation. Our approach iteratively solves a nonconvex one-dimensional subproblem globally, and it is guaranteed to converge to a coordinate-wise stationary point. We prove that this new optimality condition is always stronger than the standard critical point condition and directional point condition under a mild \textit{locally bounded nonconvexity assumption}. For comparisons, we also include a naive variant of coordinate descent methods based on sequential convex approximation in our study. When the objective function satisfies a \textit{globally bounded nonconvexity assumption} and \textit{Luo-Tseng error bound assumption}, coordinate descent methods achieve \textit{Q-linear} convergence rate. Also, for many applications of interest, we show that the nonconvex one-dimensional subproblem can be computed exactly and efficiently using a breakpoint searching method. Finally, we have conducted extensive experiments on several statistical learning tasks to show the superiority of our approach.

Coordinate Descent Methods for DC Minimization: Optimality Conditions and Global Convergence

Existing knowledge graph (KG) embedding models have primarily focused on static KGs. However, real-world KGs do not remain static, but rather evolve and grow in tandem with the development of KG applications. Consequently, new facts and previously unseen entities and relations continually emerge, necessitating an embedding model that can quickly learn and transfer new knowledge through growth. Motivated by this, we delve into an expanding field of KG embedding in this paper, i.e., lifelong KG embedding. We consider knowledge transfer and retention of the learning on growing snapshots of a KG without having to learn embeddings from scratch. The proposed model includes a masked KG autoencoder for embedding learning and update, with an embedding transfer strategy to inject the learned knowledge into the new entity and relation embeddings, and an embedding regularization method to avoid catastrophic forgetting. To investigate the impacts of different aspects of KG growth, we construct four datasets to evaluate the performance of lifelong KG embedding. Experimental results show that the proposed model outperforms the state-of-the-art inductive and lifelong embedding baselines.

Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs

Deep neural networks (DNNs) have witnessed remarkable achievement in image super-resolution (SR), and plenty of DNN-based SR models with elaborated network designs have recently been proposed. However, existing methods usually require substantial computations by operating in spatial domain. To address this issue, we propose a general frequency-oriented framework (FSR) to accelerate SR networks by considering data characteristics in frequency domain. Our FSR mainly contains dual feature aggregation module (DFAM) to extract informative features in both spatial and transform domains, followed by a four-path SR-Module with different capacities to super-resolve in the frequency domain. Specifically, DFAM further consists of a transform attention block (TABlock) and a spatial context block (SCBlock) to extract global spectral information and local spatial information, respectively, while SR-Module is a parallel network container that contains four to-be-accelerated branches. Furthermore, we propose an adaptive weight strategy for a trade-off between image details recovery and visual quality. Extensive experiments show that our FSR can save FLOPs by almost 40% while reducing inference time by 50% for other SR methods (e.g., FSRCNN, CARN, SRResNet and RCAN).

FSR: A General Frequency-oriented Framework to Accelerate Image Super-resolution Networks

While Reinforcement Learning can achieve impressive results for complex tasks, the learned policies are generally prone to fail in downstream tasks with even minor model mismatch or unexpected perturbations. Recent works have demonstrated that a policy population with diverse behavior characteristics can generalize to downstream environments with various discrepancies. However, such policies might result in catastrophic damage during the deployment in practical scenarios like real-world systems due to the unrestricted behaviors of trained policies. Furthermore, training diverse policies without regulation of the behavior can result in inadequate feasible policies for extrapolating to a wide range of test conditions with dynamics shifts. In this work, we aim to train diverse policies under the regularization of the behavior patterns. We motivate our paradigm by observing the inverse dynamics in the environment with partial state information and propose Diversity in Regulation (DiR) training diverse policies with regulated behaviors to discover desired patterns that benefit the generalization. Considerable empirical results on various variations of different environments indicate that our method attains improvements over other diversity-driven counterparts.

Open-Ended Diverse Solution Discovery with Regulated Behavior Patterns for Cross-Domain Adaptation

Memes are powerful means for effective communication on social media. Their effortless amalgamation of viral visuals and compelling messages can have far-reaching implications with proper marketing. Previous research on memes has primarily focused on characterizing their affective spectrum and detecting whether the meme's message insinuates any intended harm, such as hate, offense, racism, etc. However, memes often use abstraction, which can be elusive. Here, we introduce a novel task - EXCLAIM, generating explanations for visual semantic role labeling in memes. To this end, we curate ExHVV, a novel dataset that offers natural language explanations of connotative roles for three types of entities - heroes, villains, and victims, encompassing 4,680 entities present in 3K memes. We also benchmark ExHVV with several strong unimodal and multimodal baselines. Moreover, we posit LUMEN, a novel multimodal, multi-task learning framework that endeavors to address EXCLAIM optimally by jointly learning to predict the correct semantic roles and correspondingly to generate suitable natural language explanations. LUMEN distinctly outperforms the best baseline across 18 standard natural language generation evaluation metrics. Our systematic evaluation and analyses demonstrate that characteristic multimodal cues required for adjudicating semantic roles are also helpful for generating suitable explanations. 

What do you MEME? Generating Explanations for Visual Semantic Role Labelling in Memes

In real-world phenomena which involve mutual influence or causal effects between interconnected units, equilibrium states are typically represented with cycles in graphical models. An expressive class of graphical models, relational causal models, can represent and reason about complex dynamic systems exhibiting such cycles or feedback loops. Existing cyclic causal discovery algorithms for learning causal models from observational data assume that the data instances are independent and identically distributed which makes them unsuitable for relational causal models. At the same time, causal discovery algorithms for relational causal models assume acyclicity. In this work, we examine the necessary and sufficient conditions under which a constraint-based relational causal discovery algorithm is sound and complete for cyclic relational causal models. We introduce relational acyclification, an operation specifically designed for relational models that enables reasoning about the identifiability of cyclic relational causal models. We show that under the assumptions of relational acyclification and $\sigma$-faithfulness, the relational causal discovery algorithm RCD~\cite{maier-uai13} is sound and complete for cyclic relational models. We present experimental results to support our claim.

Learning Relational Causal Models with Cycles through Relational Acyclification

Humans and other living beings have the ability of short and long-term memorization during their entire lifespan. However, most existing Continual Learning (CL) methods can only account for short-term information when training on infinite streams of data. In this paper, we develop a new unsupervised continual learning framework consisting of two memory systems using Variational Autoencoders (VAEs). We develop a Short-Term Memory (STM), and a parameterised scalable memory implemented by a Teacher model aiming to preserve the long-term information. To incrementally enrich the Teacher's knowledge during training, we propose the Knowledge Incremental Assimilation Mechanism (KIAM), which evaluates the knowledge similarity between the STM and the already accumulated information as signals to expand the Teacher's capacity. Then we train a VAE as a Student module and propose a new Knowledge Distillation (KD) approach that gradually transfers generative knowledge from the Teacher to the Student module. To ensure the quality and diversity of knowledge in KD, we propose a new expert pruning approach that selectively removes the Teacher's redundant parameters, associated with certain unnecessary experts that have preserved the knowledge corresponding to overlapping probabilistic representations. This mechanism further reduces the complexity of the Teacher's module while ensuring the diversity of knowledge for the KD procedure. We show theoretically and empirically that the proposed framework can train a statistically diversified Teacher module for continual VAE learning which is applicable to infinite data streams.

Continual Variational Autoencoder via Continual Generative Knowledge Distillation

Community health workers (CHWs) play a crucial role in
the last mile delivery of essential health services to underserved
populations in low-income countries. Many nongovernmental
organizations (NGOs) provide training and
support to enable CHWs to deliver health services to their
communities, with no charge to the recipients of the services.
This includes monetary compensation for the work that
CHWs perform, which is broken down into a series of well defined
tasks. In this work, we partner with a NGO D-Tree
International to design a fair monetary compensation scheme
for tasks performed by CHWs in the semi-autonomous region
of Zanzibar in Tanzania, Africa. In consultation with
stakeholders, we interpret fairness as the equal opportunity
to earn, which means that each CHW has the opportunity to
earn roughly the same total payment over a given T month
period, if the CHW reacts to the incentive scheme almost rationally.
We model this problem as a reward design problem
for a Markov Decision Process (MDP) formulation for the
CHWs’ earning. There is a need for the mechanism to be
simple so that it is understood by the CHWs, thus, we explore
linear and piecewise linear rewards in the CHWs’ measured
units of work. We solve this design problem via a novel
policy-reward gradient result. Our experiments using two real
world parameters from the ground provide evidence of reasonable
incentive output by our scheme.

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Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces

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