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Aritra Dutta

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7 papers

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TMLR Journal 2026 Journal Article

Accurate Split Learning on Noisy Signals

Hang Xu
Subhajit Maity
Aritra Dutta
Xin Li
Panos Kalnis

Noise injection is applied in Split Learning to address privacy concerns about data leakage. Previous work protects Split Learning by adding noise to intermediate results during the forward pass. Unfortunately, noisy signals significantly degrade the accuracy of Split Learning training. This paper focuses on improving the training accuracy of Split Learning in the presence of noisy signals while protecting training data from reconstruction attacks. We propose two denoising techniques, namely scaling and random masking. Our theoretical results show that both of our denoising techniques accurately estimate the intermediate variables during the forward pass of Split Learning. Moreover, our experiments with deep neural networks demonstrate that the proposed denoising approaches allow Split Learning to tolerate high noise levels while achieving almost the same accuracy as the noise-free baseline. Interestingly, we show that, after applying our denoising techniques, the resulting network is more resilient to a state-of-the-art attack than the simple noise-injection approach. Our code is publicly available at: github.com/MaitySubhajit/AccurateSL.

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AAAI Conference 2026 Conference Paper

Stabilizing Policy Gradient Methods via Reward Profiling

Shihab Ahmed
El Houcine Bergou
Yue Wang
Aritra Dutta

Policy gradient methods, which have been extensively studied in the last decade, offer an effective and efficient framework for reinforcement learning problems. However, their performances can often be unsatisfactory, suffering from unreliable reward improvements and slow convergence, due to high variance in gradient estimations. In this paper, we propose a universal reward profiling framework that can be seamlessly integrated with any policy gradient algorithm, where we selectively update the policy based on high-confidence performance estimations. We theoretically justify that our technique will not slow down the convergence of the baseline policy gradient methods, but with high probability, will result in stable and monotonic improvements of their performance. Empirically, on eight continuous‐control benchmarks (Box2D and MuJoCo/PyBullet), our profiling yields up to 1.5x faster convergence to near‐optimal returns, up to 1.75x reduction in return variance on some setups. Our profiling approach offers a general, theoretically grounded path to more reliable and efficient policy learning in complex environments.

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TMLR Journal 2023 Journal Article

Personalized Federated Learning with Communication Compression

El Houcine Bergou
Konstantin Pavlovich Burlachenko
Aritra Dutta
Peter Richtárik

In contrast to training traditional machine learning~(ML) models in data centers, federated learning~(FL) trains ML models over local datasets contained on resource-constrained heterogeneous edge devices. Existing FL algorithms aim to learn a single global model for all participating devices, which may not be helpful to all devices participating in the training due to the heterogeneity of the data across the devices. Recently, Hanzely and Richt\'{a}rik (2020) proposed a new formulation for training personalized FL models aimed at balancing the trade-off between the traditional global model and the local models that could be trained by individual devices using their private data only. They derived a new algorithm, called {\em loopless gradient descent}~(L2GD), to solve it and showed that this algorithms leads to improved communication complexity guarantees in regimes when more personalization is required. In this paper, we equip their L2GD algorithm with a {\em bidirectional} compression mechanism to further reduce the communication bottleneck between the local devices and the server. Unlike other compression-based algorithms used in the FL-setting, our compressed L2GD algorithm operates on a probabilistic communication protocol, where communication does not happen on a fixed schedule. Moreover, our compressed L2GD algorithm maintains a similar convergence rate as vanilla SGD without compression. To empirically validate the efficiency of our algorithm, we perform diverse numerical experiments on both convex and non-convex problems and using various compression techniques.

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NeurIPS Conference 2021 Conference Paper

DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning

Hang Xu
Kelly Kostopoulou
Aritra Dutta
Xin Li
Alexandros Ntoulas
Panos Kalnis

Sparse tensors appear frequently in federated deep learning, either as a direct artifact of the deep neural network’s gradients, or as a result of an explicit sparsification process. Existing communication primitives are agnostic to the peculiarities of deep learning; consequently, they impose unnecessary communication overhead. This paper introduces DeepReduce, a versatile framework for the compressed communication of sparse tensors, tailored to federated deep learning. DeepReduce decomposes sparse tensors into two sets, values and indices, and allows both independent and combined compression of these sets. We support a variety of common compressors, such as Deflate for values, or run-length encoding for indices. We also propose two novel compression schemes that achieve superior results: curve fitting-based for values, and bloom filter-based for indices. DeepReduce is orthogonal to existing gradient sparsifiers and can be applied in conjunction with them, transparently to the end-user, to significantly lower the communication overhead. As proof of concept, we implement our approach on TensorFlow and PyTorch. Our experiments with large real models demonstrate that DeepReduce transmits 320% less data than existing sparsifiers, without affecting accuracy. Code is available at https: //github. com/hangxu0304/DeepReduce.

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NeurIPS Conference 2021 Conference Paper

Rethinking gradient sparsification as total error minimization

Atal Sahu
Aritra Dutta
Ahmed M. Abdelmoniem
Trambak Banerjee
Marco Canini
Panos Kalnis

Gradient compression is a widely-established remedy to tackle the communication bottleneck in distributed training of large deep neural networks (DNNs). Under the error-feedback framework, Top-$k$ sparsification, sometimes with $k$ as little as 0. 1% of the gradient size, enables training to the same model quality as the uncompressed case for a similar iteration count. From the optimization perspective, we find that Top-$k$ is the communication-optimal sparsifier given a per-iteration $k$ element budget. We argue that to further the benefits of gradient sparsification, especially for DNNs, a different perspective is necessary — one that moves from per-iteration optimality to consider optimality for the entire training. We identify that the total error — the sum of the compression errors for all iterations — encapsulates sparsification throughout training. Then, we propose a communication complexity model that minimizes the total error under a communication budget for the entire training. We find that the hard-threshold sparsifier, a variant of the Top-$k$ sparsifier with $k$ determined by a constant hard-threshold, is the optimal sparsifier for this model. Motivated by this, we provide convex and non-convex convergence analyses for the hard-threshold sparsifier with error-feedback. We show that hard-threshold has the same asymptotic convergence and linear speedup property as SGD in both the case, and unlike with Top-$k$ sparsifier, has no impact due to data-heterogeneity. Our diverse experiments on various DNNs and a logistic regression model demonstrate that the hard-threshold sparsifier is more communication-efficient than Top-$k$.

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AAAI Conference 2020 Conference Paper

On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning

Aritra Dutta
El Houcine Bergou
Ahmed M. Abdelmoniem
Chen-Yu Ho
Atal Narayan Sahu
Marco Canini
Panos Kalnis

Compressed communication, in the form of sparsiﬁcation or quantization of stochastic gradients, is employed to reduce communication costs in distributed data-parallel training of deep neural networks. However, there exists a discrepancy between theory and practice: while theoretical analysis of most existing compression methods assumes compression is applied to the gradients of the entire model, many practical implementations operate individually on the gradients of each layer of the model. In this paper, we prove that layer-wise compression is, in theory, better, because the convergence rate is upper bounded by that of entire-model compression for a wide range of biased and unbiased compression methods. However, despite the theoretical bound, our experimental study of six well-known methods shows that convergence, in practice, may or may not be better, depending on the actual trained model and compression ratio. Our ﬁndings suggest that it would be advantageous for deep learning frameworks to include support for both layerwise and entire-model compression.

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AAAI Conference 2019 Conference Paper

A Nonconvex Projection Method for Robust PCA

Aritra Dutta
Filip Hanzely
Peter Richtàrik

Robust principal component analysis (RPCA) is a well-studied problem whose goal is to decompose a matrix into the sum of low-rank and sparse components. In this paper, we propose a nonconvex feasibility reformulation of RPCA problem and apply an alternating projection method to solve it. To the best of our knowledge, this is the first paper proposing a method that solves RPCA problem without considering any objective function, convex relaxation, or surrogate convex constraints. We demonstrate through extensive numerical experiments on a variety of applications, including shadow removal, background estimation, face detection, and galaxy evolution, that our approach matches and often significantly outperforms current state-of-the-art in various ways.

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