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Fountain Codes
Consider a setting where a large file is disseminated to a wide audience who may want to access it at various times and have transmission links of different quality. Current networks useExpand
Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server, while keeping the training data decentralized. Expand
On the Delay-Storage Trade-Off in Content Download from Coded Distributed Storage Systems
We study how coding in distributed storage reduces expected download time, in addition to providing reliability against disk failures. Expand
Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms
Communication-efficient SGD algorithms, which allow nodes to perform local updates and periodically synchronize local models, are highly effective in improving the speed and scalability of distributed SGD. Expand
Rateless Codes for Near-Perfect Load Balancing in Distributed Matrix-Vector Multiplication
We propose a rateless fountain coding strategy that achieves the best of both worlds -- we prove that its latency is asymptotically equal to ideal load balancing, and it performs as much as 3x speed-up over uncoded schemes. Expand
Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD
We propose AdaComm, an adaptive communication strategy that starts with infrequent averaging to save communication delay and improve convergence speed, and then increases the communication frequency in order to achieve a low error floor. Expand
Coding for fast content download
We study the fundamental trade-off between storage and content download time. Expand
Using Straggler Replication to Reduce Latency in Large-scale Parallel Computing
In cloud computing jobs consisting of many tasks run in parallel, the tasks on the slowest machines (straggling tasks) become the bottleneck in the completion of the job. Expand
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
In federated optimization, heterogeneity in the clients' local datasets and computation speeds results in large variations in the number of local updates performed by each client in each communication round. Expand
Efficient Redundancy Techniques for Latency Reduction in Cloud Systems
In cloud computing systems, assigning a task to multiple servers and waiting for the earliest copy to finish is an effective method to combat the variability in response time of individual servers and reduce latency. Expand