Workshop

https://www.youtube.com/watch?v=gfGTd7PXK54&list=PLMPUUgLIYH1ZJVEXTTZT82ipTprSu1hn8&index=1

Learning at the Wireless Edge

• Vince Poor (Princeton University)

• Two aspects

  • Using ML to optimize communication networks

  • Learning on mobile devices (the focus of today's talk)

• Today's talk: focus on federated learning

  • Motivation

  • Federated learning over wireless channels (scheduling)

  • Privacy protection in federated learning (differential privacy)

  • Some research issues

• ML

  • Tremendous progress in recent years (more data, increase in computational power)

  • Standard ML: implement in centralized manner (data center, cloud), full access to the data

  • SOTA models:

    • Standard software tools, specialized hardware

• Wireless edge

  • Centralized ML are not suitable for many emerging applications

    • Self-driving cars, first responder networks, healthcare networks

  • What makes the application different:

    • Data is born at the edge (phone, IoT devices)

    • Limited capacity uplinks

    • Low latency & high reliability

    • Data privacy / security

    • Scalability & locality

  • Motivate moving learning closer to the network edge

• Federated learning over wireless channels (scheduling)

  • 
  • Wireless: communication to the AP needs to go through wireless channels

    • Shared, resource-constrained

      • Only limited number of devices can be selected in each update round

      • Transmissions are not reliable due to interference

    • Questions

      • How should we schedule devices to update the trained weights?

      • How does the interference affect the training?

  • Scheduling mechanisms

    • Random scheduling: aggregator select N out of K users at random

    • Round Robin: divide into group

    • Proportional Fair: strongest SNRs

  • 
  • 
• Design metric: age of information (AoI)

  • Age-based scheduling scheme for federated learning in mobile edge networks

  • Optimization algorithm in each iteration round

  • Wireless round robin

• Privacy in federated learning

  • "privacy preserving": data remains on end-user devices

  • But end-user data can be inferred from the parameter (or gradient) updates

  • Approach: use differential privacy to protect end-user data

    • Refers to a type of privacy in which two datasets, one with private information and one without it, but otherwise identical, cannot be distinguished by a statistical query (with high probability)

  • Trade-off between privacy and accuracy

• Other issues

  • Model efficiency

    • Resources on end-user devices are limited (e.g., energy, storage, computational power)

    • Trade-offs between # of layers, # of neurons per layer, accuracy

  • Communication efficiency

  • Limited data at the edge

    • Local data is sparse

    • Incorporating domain and physical knowledge

  • Security & Privacy

    • Robustness to malicious end-user devices & adversarial training examples

    • Other approaches to end-user privacy

Challenges in ML and the way forward

• Ariela Zeira, Intel Labs

• Challenges in DL

  • Compute efficiency

  • Memory overhead

  • Data efficiency

  • Online learning

  • Robustness

  • Knowledge Representation

• Hyper Dimensional Computing

  • New paradigm for energy-efficient, noise-robust and fast alternatives to standard ML

Adventures in Learning-Based Rate Control

• Brighten Godfrey, UIUC

• TCP protocols: point solutions designed for specific environments, and far from optimal

• Why does traditional CC architecture struggle?

  • TCP Reno, CUBIC, FAST, Scalable, HTCP

  • "Hardwired" control actions

    • Underlying conditions --> ideal control action

    • 
    • Not enough information about what's happening in the network

• What is the right rate to send?

  • Network is a blackbox: but we can send at some rate and see what happens

    • Collect observations: throughput, loss rate, latency. We can summarize that in a utility function

• A change in perspective

  • Traditional perspective: simple network model + well-crafted rules --> predictable results

  • "Black-box" perspective: the world is complex. Quantifying goal and observing effect of actions yields good decisions

  • A fit for learning!

    • Diverse, opaque environments

    • Only a trickle of information

    • Infer good action at millisecond timescales

• Software components

  • 
  • Paper: PCC

  • Control algorithm: heuristic hill-climbing algorithm

    • Noise in measurement? randomized controlled trials

  • Where is the congestion control?

    • Equilibrium depends on utility function

    • Selfish utility-maximizing decision --> non-cooperative game

• Promising performance

• Upgrade: PCC Vivace (NSDI 2018)

  • Leveraging powerful tools from online learning theory

  • New utility function framework

    • Latency-awareness

    • Strictly concave --> equilibrium guarantee

    • Weighted fairness among senders

  • New control algorithm

    • Gradient-ascent --> convergence speed / stability

    • Deals with measurement noise

  • Performance: great improvement in latency & responsiveness, but still suboptimal in extremely dynamic networks (i.e. wireless)

• Deep RL on congestion control (ICML 2019)

Aurora agent architecture

• scale-free values to aid robustness

• History length: what lengths work well

• Training

  • Simulated environment

    • Order of magnitude faster than emulation

    • Each episode chooses link parameters from a range

  • Setting appropriate discount factor

    • Maximize expected cumulative discounted return

• Future

  • Multi-gent scenarios: training, competition

  • Online training: challenge is to improve outcomes with limited additional training data

• New uses

  • Scavenger transport

    • SIGCOMM 2020: Proteus: Scavenger Transport and Beyond

    • Different applications: software updates, CDN warmup, cloud storage replication, online video, real-time streaming, search

      • Elastic timing, inelastic timing

    • Scavenger design goals

      • Yielding: minimally impact primary flows

      • Performance: high utilization, low latency when only scavengers exist

      • Flexibility: dynamically switch, avoid separate implementation

    • Utility functions

      • Primary, Scavenger, Hybrid

      • RTT deviation as a competition indicator

        • Definition: standard deviation of observed RTT samples

        • Intuition: earlier signal of dynamics of buffer occupancy during flow competition

        • Proteus: yields more effectively

      • Cross-layer design

        • Dynamic threshold based on the application requirement (buffer occupancy)

        • Hybrid mode: to get the bandwidth when they need it

        • Improving QoE (rebuffer raito)

• Rate control robustness (HotNets 2019)

  • Keeping up with rapid change

    • Recent acceleration of innovation in rate control

    • 
    • Approach: ML as an adversary

      • 
      • It gets rewarded when it finds environmental parameters that cause this algorithm under test to perform poorly (looking for the hard cases)

        • Do this carefully, rewarded for suboptimal performance of the algorithm, and smoothing to make it more useful results to see

      • Reward = -1 * protocol score + optimal score - smoothing penalty

      • Implement: ABR video, CC

• Lessons learned

  • What worked

    • Modular architecture

      • New control algorithms

      • New utility functions open new issues

    • Learning-based control can improve performance over traditional protocols

      • Industry implementations

  • Open challenges & opportunities

    • Performance: fast decisions v.s careful decisions

      • Even one RTT can be a long time - especially in fluctuating wireless environments

    • Understanding protocol robustness

    • Existing opportunities in system design

      • Complexity in systems, environments, and application needs lead to opportunity for inference

      • Restructure systems for a learning mindset